A resource sharing platform for resource-constrained software defined cognitive radio networks

Halloush, Rami; Musa, Ahmed; Halloush, Mohammed; Almalkawi, Islam T.

doi:10.1109/sds.2018.8370419

Cited by 2 publications

(12 citation statements)

References 21 publications

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“…13 Therefore, its useful to provide a brief overview of the basic components of the aforementioned platform, namely, normal graphs and the BP algorithm. 13 Therefore, its useful to provide a brief overview of the basic components of the aforementioned platform, namely, normal graphs and the BP algorithm.…”

Section: Belief Propagation Over Normal Graphsmentioning

confidence: 99%

“…In the authors' previous work, 13 P(x j = j ), where j ∈ {0, 1}, is included in a message sent from SU j to the FC reflecting SU j 's belief whether the PU channel being sensed is available ( j = 0) or busy ( j = 1). The intrinsic probability P(x j = j ) is the prior probability of x j .…”

Section: Figurementioning

confidence: 99%

“…13 The main contribution of the aforementioned platform was employing normal graphs 14,15 to model resource-limited CRNs. 13 The main contribution of the aforementioned platform was employing normal graphs 14,15 to model resource-limited CRNs.…”

Section: Introductionmentioning

confidence: 99%

“…13 The main contribution of the aforementioned platform was employing normal graphs 14,15 to model resource-limited CRNs. It was shown in the author's previous work 13 that the BP algorithm achieves high accuracy. It was shown in the author's previous work 13 that the BP algorithm achieves high accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…The scheme we propose in this paper is based on the spectrum sensing platform that we proposed in our previous work. 13 The main contribution of the aforementioned platform was employing normal graphs 14,15 to model resource-limited CRNs. The belief propagation (BP) algorithm 15,16 was used to decide whether a PU channel is busy or idle, and in case it is idle, it determines the SUs that are allowed to transmit in a way that guarantees not exceeding the limited resource.…”

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confidence: 99%

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A rate‐maximizing spectrum sharing algorithm for cognitive radio networks with generic resource constraints

Halloush

Almalkawi

et al. 2019

Trans Emerging Tel Tech

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Algorithms for spectrum sharing over resource‐limited cognitive radio networks are often designed to solve specific problems. This means that a certain algorithm deals specifically with a certain limited resource, and is not suitable for other resources. This limitation violates the software‐defined networking philosophy, where a scheme has to be reprogrammable to cope with different limited resources that can dynamically arise depending on network conditions. In this work, we investigate the problem of spectrum sharing in resource‐limited cognitive radio networks. Specifically, we introduce a novel spectrum sharing algorithm that is compatible with software‐defined networks, in the sense that it can be reprogrammed to support multiple constraints on resources of different types. A main feature of the proposed scheme is that computations could be distributed across multiple processing units to reduce computational complexity on each unit. In addition, the proposed algorithm is equipped with a fairness scheme. Simulation results demonstrate the efficacy of the proposed scheme.

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Section: Belief Propagation Over Normal Graphsmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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A rate‐maximizing spectrum sharing algorithm for cognitive radio networks with generic resource constraints

Halloush

Almalkawi

et al. 2019

Trans Emerging Tel Tech

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System analysis of the cognitive radio system operation algorithm efficiency

Blagodatsky

Копысов

Khvorenkov

et al. 2020

IOP Conf. Ser.: Mater. Sci. Eng.

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The report presents an algorithm for optimal control of a cognitive radio communication system. The algorithm is based on the application of decision-making criteria in a dynamic environment. The basis of the algorithm is to solve the problem of maximizing the system efficiency coefficient in a given mode. Thus, the cognitive radio communication system can choose the most optimal mode of operation, both in a specific situation, and choose the general preferred mode of operation for a comprehensive assessment of the external situation. From the effectiveness of the radio system in analog and digital modes, we can conclude that in the event of time limitations it is more useful to control the transmitter power (increase the energy of the radio line). However, when working in digital modes, the decrease in efficiency is much slower than when using analog modes. An analysis of the operation under conditions of restrictions on the transmission power also shows a slower decrease in efficiency in digital modes with slow consumption of resources, while consumption increases sharply in analog mode.

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A resource sharing platform for resource-constrained software defined cognitive radio networks

Cited by 2 publications

References 21 publications

A rate‐maximizing spectrum sharing algorithm for cognitive radio networks with generic resource constraints

A rate‐maximizing spectrum sharing algorithm for cognitive radio networks with generic resource constraints

System analysis of the cognitive radio system operation algorithm efficiency

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