Routing Protocols on Cognitive Radio Networks: Survey

Elrhareg, Houda; Ridouani, Mohammed; Hayar, Aawatif

doi:10.1109/isc246665.2019.9071779

Cited by 8 publications

(3 citation statements)

References 34 publications

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“…Moreover, the optimization of the energy consumption, the desired QoS, and the spectrum management should be considered while designing routing algorithms and protocols for CR networks. Routing protocols are further detailed in [26].…”

Section: B Cognitive Radio Functionsmentioning

confidence: 99%

A Review on SDR, Spectrum Sensing, and CR-based IoT in Cognitive Radio Networks

Kassri¹,

En‐Nouaary²,

Bah³

et al. 2021

IJACSA

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The inherent scarcity of frequency spectrum, along with the fixed spectrum allocation adopted policy, has led to a dire shortage of this indispensable resource. Furthermore, with the tremendous growth of wireless applications, this problem is intensified as the unlicensed frequency spectrum becomes overcrowded and unable to meet the requirement of emerging radio devices operating at higher data rates. Additionally, the already assigned spectrum is underutilized. That has prompted researchers to look for a way to address spectrum scarcity and enable efficient use of the available spectrum. In this context, Cognitive Radio (CR) technology has been proposed as a potential means to overcome this issue by introducing opportunistic usage to less congested portions of the licensed spectrum. In addition to outlining the fundamentals of Cognitive Radio, including Dynamic Spectrum Access (DSA) paradigms and CR functions, this paper has a three-fold objective: first, providing an overview of Software Defined Radio (SDR), in which the architecture, benefits, and ongoing challenges of SDR are presented; second, giving an extensive review of spectrum sensing, covering sensing types, narrowband and wideband sensing schemes with their pros and cons, Machine Learningbased sensing, and open issues that need to be further addressed in this field; third, exploring the use of Cognitive Radio in the Internet of Things (IoT) while highlighting the crucial contribution of CR in enabling IoT. This Review is elaborated in an informative fashion to help new researchers entering the area of Cognitive Radio Networks (CRN) to easily get involved.

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Section: B Cognitive Radio Functionsmentioning

confidence: 99%

A Review on SDR, Spectrum Sensing, and CR-based IoT in Cognitive Radio Networks

Kassri¹,

En‐Nouaary²,

Bah³

et al. 2021

IJACSA

View full text Add to dashboard Cite

show abstract

“…Routing in DSA (or Cognitive Radio Networks, CRNs) has been fairly well investigated in the past [4], [6], [7], [11]- [15]. The main idea of existing DSA routing protocols is to select "best available channel" (loosely referred to as, spectrum selection) and "an optimal next-hop node" while accounting for several parameters (like channel identification, switching delay, PU modeling etc.).…”

Section: Related Workmentioning

confidence: 99%

“…Jagannath et al [4] proposed a joint route, channel and power control algorithm to maximize the utilization of available resources and ensure delivery of packets within the deadline constraints. Refer to a recent survey [11] for an overview on DSA routing protocols. Existing works on DSA routing either assume a DSA network model with an unspecified primary band [6] or one restricted primary band -mostly TV [4], [7].…”

Section: Related Workmentioning

confidence: 99%

Cross-layer Band Selection and Routing Design for Diverse Band-aware DSA Networks

Upadhyaya¹,

Shah²,

Reed³

2020

Preprint

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As several new spectrum bands are opening up for shared use, a new paradigm of Diverse Band-aware Dynamic Spectrum Access (d-DSA) has emerged. d-DSA equips a secondary device with software defined radios (SDRs) and utilize whitespaces (or idle channels) in multiple bands, including but not limited to TV, LTE, Citizen Broadband Radio Service (CBRS), unlicensed ISM. In this paper, we propose a decentralized, online multi-agent reinforcement learning based cross-layer BAnd selection and Routing Design (BARD) for such d-DSA networks. BARD not only harnesses whitespaces in multiple spectrum bands, but also accounts for unique electro-magnetic characteristics of those bands to maximize the desired quality of service (QoS) requirements of heterogeneous message packets; while also ensuring no harmful interference to the primary users in the utilized band. Our extensive experiments demonstrate that BARD outperforms the baseline dDSAaR algorithm in terms of message delivery ratio, however, at a relatively higher network latency, for varying number of primary and secondary users. Furthermore, BARD greatly outperforms its single-band DSA variants in terms of both the metrics in all considered scenarios.

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An Extended ACO Based Routing Algorithm for Cognitive Radio Network

Raj

Murmu

2021

Algorithms for Intelligent Systems

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Routing Protocols on Cognitive Radio Networks: Survey

Cited by 8 publications

References 34 publications

A Review on SDR, Spectrum Sensing, and CR-based IoT in Cognitive Radio Networks

A Review on SDR, Spectrum Sensing, and CR-based IoT in Cognitive Radio Networks

Cross-layer Band Selection and Routing Design for Diverse Band-aware DSA Networks

An Extended ACO Based Routing Algorithm for Cognitive Radio Network

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