Designing a MAC Algorithm for Equitable Spectrum Allocation in Cognitive Radio Wireless Networks

López, Danilo; Rojas, Camilo; Zapata, Diego Fernando; Trujillo, Edwin Rivas

doi:10.1007/s11277-017-4873-0

Cited by 13 publications

(14 citation statements)

References 29 publications

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“…There is a clear trade-off between range-based and range-free techniques with respect to positioning accuracy and implementation cost. [24][25][26] So, to balance this trade-off, the node localization is defined as a least square problem and optimized using several metaheuristic techniques. Differential evolution algorithm (DEA) proposed by Storn 27 is applied for localization in Harikrishnan et al and Annepu and Rajesh.…”

Section: Relative Workmentioning

confidence: 99%

Implementation of an efficient extreme learning machine for node localization in unmanned aerial vehicle assisted wireless sensor networks

Annepu

Anbazhagan

2019

Int J Communication

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Accurate node localization in wireless sensor networks (WSNs) is an essential for many networking protocols like clustering, routing, and network map building. The classical localization techniques such as multilateration and optimization-based least square localization (OLSL) techniques estimate position of unknown node (UN) from the distance measured between all anchor nodes (ANs) and UNs. On the other hand, node localization using fixed terrestrial ANs suffers from poor localization accuracy because the ground to ground (GG) channel link is not reliable. By contrast, the mobile anchor deployed in unmanned aerial vehicle (UAV) provides high localization accuracy through reliable air to ground (AG) channel link. Still, the nonlinear distortion introduced in the wireless channel makes the distance measurement noisy. This noisy distance measurement also limits localization accuracy of classical localization techniques. Hence, the highly nonlinear artificial neural network (ANN) models such as multilayer perceptron (MLP) models can be applied effectively for node localization in UAV-assisted WSNs. However, the MLP suffers from slow training speed, which limits its usage in real-time applications.So, the extreme learning machine (ELM) is found to be a better alternative because it works on empirical error minimization theory, and its learning process requires only a single iteration. The detailed simulation analysis supports the proposed ELM localization scheme in terms of both localization accuracy and computational complexity.

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Section: Relative Workmentioning

confidence: 99%

Implementation of an efficient extreme learning machine for node localization in unmanned aerial vehicle assisted wireless sensor networks

Annepu

Anbazhagan

2019

Int J Communication

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“…Two techniques (ANFIS and FAHP) are used for categorizing. Results of each technique are compared to each other and to a channel assignment algorithm that does not perform any classification regarding a new measure of quantifiable fairness to measure system performance [25]. EElhachmi et al applied a dynamic genetic algorithm (GA) for channel allocation in cognitive radio.…”

Section: Related Workmentioning

confidence: 99%

ILFCS: an intelligent learning fuzzy-based channel selection framework for cognitive radio networks

Arnous

El-Desouky

Sarhan

et al. 2018

J Wireless Com Network

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Cognitive radio networks (CRNs) have been introduced as a promising solution to optimize the use of available radio-frequency spectrum. The key idea in CRNs is the proper selection of available sensed channels. In this paper, an intelligent distributed channel selection strategy is proposed for cognitive radio ad-hoc networks aiming to assist them in selecting the best channel for transmission. The proposed strategy classifies the available channels based on the primary users'(PUs) utilization, the number of cognitive radio neighbors using the channels, and the capacity of available channels. The Fuzzy Logic technique is used to determine a channel's weight value by combining these parameters. The channels with the highest weight value are selected for transmission. The proposed strategy takes into account false alarm (FA) and miss detection (MD) metrics to classify the sensed channels into four categories (FA, MD, ON and OFF) based on K-means learner. This classification helps the strategy to avoid accessing occupied channels. Simulation results based on NS2 simulation approved that the proposed strategy is effective compared to other strategies concerning selecting the best channel and achieving higher channel utilization.

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“…With the culmination of this research work, the academic community is given an important advance in IPTV on which new developments, research and degree projects can be carried out. The possibility of implementing the evaluation of the quality of the image perceived by cognitive and primary users in a cognitive radio network [7] and [8] is left for study and subsequent investigation.…”

Section: A Perception Analysis Of the Transmitted Image Qualitymentioning

confidence: 99%

Analysis of the image quality transmitted by an IPTV system

S¹,

Avellaneda²,

Rodríguez³

2018

IJET

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This article compares the results obtained when evaluating the PSNR (Peak Signal-to-Noise Ratio) metric when television and video content is delivered on demand on an IPTV-IMS platform using the RTSP and HTTP protocols. Several scenarios are evaluated for this. Initially, television is broadcast using the RTSP protocol, switching between wireless and wired networks and varying the network load. Later this process is repeated for the HTTP protocol and finally video is broadcast on demand repeating the load conditions and network type. Each of these scenarios starts with a user receiving the transmission and ends when 10 users are connected. The results of these tests are plotted and compared in order to determine the quality of the image received in the different scenarios implemented. Keyword-Real Time Streaming Protocol (RTSP), Hypertext Transfer Protocol (HTTP), PSNR, MSE, IPTV-IMS I. INTRODUCTION At present, one of the most demanding transmission flows in terms of resources in communications networks is related to the delivery of multimedia content (television and video on demand over IP). It is therefore important to investigate, study and evaluate the level of quality with which the signals or images reach the receivers or end users. For this reason, this article presents the results found when evaluating the PSNR metric in order to determine the relationship between the maximum energy of a signal (image) versus background noise; and for this the present article is divided into three large Sections that includes the design of the IPTV platform on which the tests were carried out, to subsequently share the results found and the final conclusions. II. IPTV PLATFORM DESIGN. For the implementation of an IPTV platform, there must be a minimum of entities that guarantee the correct functioning of the platform. Initially, an IMS core must be available, which was implemented in software through the Open IMS Core [1] and which includes three call session control entities (CSCF) and a user database. However, three more elements are required, which are what will ultimately shape the implementation of an IPTV system. These elements correspond to the IPTV application server (IPTV AS), the IMS Client and the Media Server. The first two as well as the core have been developed in free code by the Communications Research Group of the University of Cape Town [2] in South Africa. The IPTV application server developed under the name of UCT IPTV Advanced allows loading the address table in which the search of the selected contents will be carried out. Regarding the IMS client, there are several free code options available to work with the selected project, which have different characteristics [2]. However, the only one that supports IPTV is the UCT IMS Client, which has a simple interface in which there are several options such as client registration, preference settings, different call options, video call, instant messaging and request of channels or videos available. However, this client is only available for computers, so if ...

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Designing a MAC Algorithm for Equitable Spectrum Allocation in Cognitive Radio Wireless Networks

Cited by 13 publications

References 29 publications

Implementation of an efficient extreme learning machine for node localization in unmanned aerial vehicle assisted wireless sensor networks

Implementation of an efficient extreme learning machine for node localization in unmanned aerial vehicle assisted wireless sensor networks

ILFCS: an intelligent learning fuzzy-based channel selection framework for cognitive radio networks

Analysis of the image quality transmitted by an IPTV system

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