Optimizing the Capacity of Cognitive Radio Networks with Power Control and Variable Spectrum Allocation

Benghabrit, Nawel; Kaddour, Mejdi

doi:10.2478/ttj-2018-0011

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…When there is persistent interference in the spectrum, the cognitive radio comes into play. It monitors the environment, identifies interference, and adjusts network parameters to transfer communications to a less congested band of the spectrum while continually monitoring the interference in the new band [39].…”

Section: ) Cognitive Radiomentioning

confidence: 99%

Low-Power Wide-Area Networks: A Broad Overview of Its Different Aspects

et al. 2022

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Low-power wide-area networks (LPWANs) are gaining popularity in the research community due to their low power consumption, low cost, and wide geographical coverage. LPWAN technologies complement and outperform short-range and traditional cellular wireless technologies in a variety of applications, including smart city development, machine-to-machine (M2M) communications, healthcare, intelligent transportation, industrial applications, climate-smart agriculture, and asset tracking. This review paper discusses the design objectives and the methodologies used by LPWAN to provide extensive coverage for low-power devices. We also explore how the presented LPWAN architecture employs various topologies such as star and mesh. We examine many current and emerging LPWAN technologies, as well as their system architectures and standards, and evaluate their ability to meet each design objective. In addition, the possible coexistence of LPWAN with other technologies, combining the best attributes to provide an optimum solution is also explored and reported in the current overview. Following that, a comparison of various LPWAN technologies is performed and their market opportunities are also investigated. Furthermore, an analysis of various LPWAN use cases is performed, highlighting their benefits and drawbacks. This aids in the selection of the best LPWAN technology for various applications. Before concluding the work, the open research issues, and challenges in designing LPWAN are presented.INDEX TERMS Low-power wide-area networks, wireless networks, Internet of Things, design objectives, network topology, architecture, and applications. I. INTRODUCTIONThe Internet of Things (IoT) has the potential to change the way we live and work by providing its essential services to all the smart devices which are connected to it. IoT alleviates various challenges of wireless networks such as robust internet connectivity among a large population of smart devices, energy constraints in mobile edge devices, fast data transfer, efficient bandwidth utilization, and increased data gathering capability/reliability at the receiver. To achieve these goals, the devices must be optimally connected to the network, resulting in a fast exchange of sensed data facilitating intelligent decision-making to control the physical world phenomenon yielding a smart ecosystem. Several independent studies have predicted that in coming years IoT plays a crucial role in various social and commercial applications such as smart healthcare, intelligent transporta-tion, climate-smart agriculture, rescue operations, logistics, smart cities, industries, utilities, smart buildings, consumer electronics, security, asset tracking, smart waste management systems, cognitive manufacturing, and Machine-to-Machine (M2M) communications [1]-[5]. Furthermore, it has been estimated that by 2025, the adoption of smart M2M gadgets and electronic goods can overtake the number of human subscribers utilizing smartphones, desktops, notebooks, and similar objects. According to the analys...

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Section: ) Cognitive Radiomentioning

confidence: 99%

Low-Power Wide-Area Networks: A Broad Overview of Its Different Aspects

et al. 2022

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“…Better sensing information leads to better transmit power control. In [8] a genetic algorithm is used to allocate recourses to maximize overall network capacity and optimize the power. Genetic algorithm (GA) outperforms the greedy algorithm.…”

Section: Introductionmentioning

confidence: 99%

Transmit power control and data rate enhancement in cognitive radio network using computational intelligence

Goel

Singh

Goel

2022

IJECE

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Underutilized radio frequencies are the chief apprehension in advance radio communication. The radio recourses are sparse and costly and their efficient allocation has become a challenge. Cognitive radio networks are the ray of hope. Cognitive radio networks use dynamic spectrum access technique to opportunistically retrieve and share the licensed spectrum. The licensed users are called primary users and the users that opportunistically access the licensed spectrum all called secondary users. The proposed system is a feedback system that work on demand and supply concept, in which secondary receivers senses the vacant spectrum and shares the information with the secondary transmitters. The secondary transmitters adjust their transmission parameters of transmit power and data rate in such a way that date rate is maximized. Two methods of spectrum access using frequency division multiple access (FDMA) and Time division multiple access (TDMA) are discussed. Interference temperature limit and maximum achievable capacity are the constraints that regulate the entire technique. The aim of the technique is to control the transmitter power according to the data requirements of each secondary user and optimizing the resources like bandwidth, transmit power using machine learning and feed forward back propagation deep neural networks making full use of the network capacity without hampering the operation of primary network.

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“…This led to the spectrum sensing concept of identifying the unused spectrum holes. Spectrum sensing can be defined as the ability to detect the presence or absence of a licensed user in the channel, while the most challenging task is to sense in shallow SNR regime [2] In current communication networks, the average spectrum utilization is between 15% to 85%. Cognitive Radio (CR) is a solution to increase the spectrum utilization and ultimately, the network capacity leading to generating new revenue streams with a higher quality of service.…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Spectrum Hole Detection using Convex Optimization And Tensor Analysis In Cognitive Radio Network

Agajo¹,

Joseph²,

Eronu³

et al. 2019

JBEMI

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The issue of speed and accuracy is one major challenge in the area of Spectrum hole detection in Cognitive Radio Network(CRN), owing to some of the techniques used in the previous past, noise is sometimes recorded against spectrum hole, and this is mostly due to the method adopted , the need for a more compact procedure as become necessary. An Algorithm for Spectrum Hole Detecting using Convex Optimization and Tensor analysis in Cognitive Radio Network seeks to present a way out of it. The tensor analysis will provide an infinite representation Spectrum data from the wideband, while Convex optimization will help split the large data by grouping it into various spectrum segment, based on the objective function, this grouping will help improve on the speed of Spectrum hole detection. Principal Component Analysis(PCA) checks the level of correction using orthogonal transformation, the use of Eigen Values and Eigen Vectors will further help linearize the function by finding the roots. Covariance matrix will help further check how the variable varies together. It describes the dimension of the spectrum data. Diagonisation is used to extract the matrix with the spectrum data using singular value decomposition; finally, Bayesian inference will optimise decision making for spectrum data.

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Optimizing the Capacity of Cognitive Radio Networks with Power Control and Variable Spectrum Allocation

Cited by 6 publications

References 14 publications

Low-Power Wide-Area Networks: A Broad Overview of Its Different Aspects

Low-Power Wide-Area Networks: A Broad Overview of Its Different Aspects

Transmit power control and data rate enhancement in cognitive radio network using computational intelligence

An Algorithm for Spectrum Hole Detection using Convex Optimization And Tensor Analysis In Cognitive Radio Network

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