Joint Channel Assignment and Power Allocation Based on Maximum Concurrent Multicommodity Flow in Cognitive Radio Networks

Salah, Ahmed; Abdel-Atty, Heba M.; Rizk, Rawya

doi:10.1155/2018/3545946

Cited by 12 publications

(9 citation statements)

References 21 publications

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“…An overview of security threats and challenges concerning CRN was proposed in [11]. The impact of SU node transmission on channel capacity scaling factor based on maximum concurrent multi commodity flow (MCMCF) was designed in [12] to ensure fairness. Yet another Bayesian learning approach for both detection and mitigation was presented in [13] to achieve desired trade-off between security and performance for taming cross-layer attack.…”

Section: Related Workmentioning

confidence: 99%

Optimised Levenshtein centroid cross‐layer defence for multi‐hop cognitive radio networks

Ganesh

Kumar

Kumar³

2020

IET Communications

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Cognitive radio networks (CRN) make use of dynamic spectrum access to communicate opportunistically. Unlicensed users severely affect the spectrum sensing outcomes in CRN. Primary user emulation attack (PUEA) and spectrum sensing data falsification (SSDF) have become a paramount concern in CRNs. It is especially challenging when both masquerading (in the physical layer) and falsification (in the data link layer) occur by providing false spectrum reports. Existing methods to detect such attacks cannot be utilised in scenarios with multi-hop CRN. In this study, to mitigate attack against PUEA and SSDF, a method called optimised sensing and Levenshtein nearest centroid classification (OS-LNCC) for multi-hop CRN is presented. First, a network model for multi-hop CRN is designed. Next, a probable density optimal logical sensing model is designed to alleviate the problems related to falsification of spectrum reports. Here, the falsification of spectrum reports is overcome by exploiting dual factors, that is, probability for false alarm and probability for detection according to the departure rate of primary user (PU). With these dual factors, optimal logical sensing is made, therefore improving the throughput with minimum delay. Finally, each cognitive radio (CR) user evaluates its current sensing information to existing sensing classes through the Levenshtein distance function. Based on quantitative variables, the prediction function of each sensing class is measured using nearest centroid (NC) classifier and the sensing report is classified into either presence or absence of PU. These predictive classes are then integrated at the fusion centre so that robust mitigation against PUEA and SSDF is made. Computer simulation outputs show that OS-LNCC method performs better than the conventional methods using metrics such as sensing delay by 47%, percentage of error in prediction by 46% and throughput by 45%.

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Section: Related Workmentioning

confidence: 99%

Optimised Levenshtein centroid cross‐layer defence for multi‐hop cognitive radio networks

Ganesh

Kumar

Kumar³

2020

IET Communications

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“…PU activity on all channels was observed, and handoff decision was taken according to an online learning algorithm. Salah et al also considered the routing problem where CRN uses TV white spaces. The main objective of the work is to utilize all idle channels efficiently under SINR constraint.…”

Section: Channel Assignment In Literaturementioning

confidence: 99%

Cross‐layer perspective for channel assignment in cognitive radio networks: A survey

Gupta

Dhurandher

2019

Int J Communication

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Summary With the advent of various emerging wireless products, the usage of limited spectrum has grown exponentially in the recent years. In the next few years, that mobile data traffic globally is expected to grow up to 50 EB/month, which is nearly a five times increase over year 2018. Therefore, it will become extremely difficult to satisfy the ever increasing demand through the current fixed spectrum assignment policy in which spectrum band is exclusively used for the particular applications, and it has also led to underutilization of a significant portion of the spectrum (like TV bands). Cognitive radio networks has emerged as a possible solution for the problem which makes dynamic spectrum access possible for unlicensed user when licensed user is not active. Among various operations of cognitive radio, channel assignment to the unlicensed user is very important. Further, wireless regional area network is one of the most important application of cognitive radio, which provides wireless broadband to the rural area using vacant TV channels. This paper discusses channel assignment techniques considering various functionalities for cognitive radio networks in respect of wireless regional area network in the existing literature. Initially, a comprehensive introduction to both cognitive radio networks and wireless regional area networks is provided, and in the end, the paper summarizes the various issues and research challenges in the channel assignment for wireless regional area networks.

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“…The spectrum sharing scheme in the work of Lopez-Ramos et al 17 aimed at maximizing the throughput (sum-rate) of the CRN while protecting the PU through an average interference constraint. In the work of Salah et al, 21 the authors investigated joint channel, power, and route assignment for SU networks. The proposed scheme is based on antenna selection at the transmitter and receiver sides.…”

Section: Related Workmentioning

confidence: 99%

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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Joint Channel Assignment and Power Allocation Based on Maximum Concurrent Multicommodity Flow in Cognitive Radio Networks

Cited by 12 publications

References 21 publications

Optimised Levenshtein centroid cross‐layer defence for multi‐hop cognitive radio networks

Optimised Levenshtein centroid cross‐layer defence for multi‐hop cognitive radio networks

Cross‐layer perspective for channel assignment in cognitive radio networks: A survey

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

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