Fusion based spectrum decision framework for cognitive radio users

Akhtar, Ahmad Naeem; Rashdi, Adnan; Arif, Fahim

doi:10.1109/wowmom.2015.7158193

Cited by 4 publications

(5 citation statements)

References 14 publications

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“…For each one of them, the mean and standard deviation of the performance indicators DU e DR where evaluated for several values channel status error probability and observation and prediction window sizes. In the majority of cases, the method here proposed outperformed those obtained by the three methods presented in [6].…”

Section: Simulation Results and Comparison With Other Methodsmentioning

confidence: 73%

“…Gulnur et al [6] recently proposed three methods to deal with the spectrum vacancy prediction problem that perform well when compared with other techniques found in the literature. The methods are:…”

Section: Simulation Results and Comparison With Other Methodsmentioning

confidence: 99%

“…The recent work of Akhtar el al [6] presents a novel spectrum decision scheme, which according to the authors, remains an unexplored area in cognitive radio research. The proposed decision making scheme is based on the fusion of three key channel parameters i.e.…”

mentioning

confidence: 99%

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Spectral Vacancies Prediction Method for Cognitive Radio Applications

Canavitsas

Mello

Grivet

2016

J. Microw. Optoelectron. Electromagn. Appl.

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Abstract-Cognitive radio technology is in fast development and is considered a possible solution to improve the efficiency of radio spectrum use. Many studies have been recently carried out in order to improve spectrum-sharing techniques between primary and secondary users. This paper investigates one of the basic decision problems faced by a cognitive radio: given a time window of a specific size, a secondary user (SU) should decide if it will use it or not, minimizing the chances of collision with a primary user (PU). For this purpose, an algorithm is proposed that does not require previous information about PU occupancy behavior. The proposed algorithm shows very good performance when compared to three other methods recently proposed to tackle with this problem.

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Section: Simulation Results and Comparison With Other Methodsmentioning

confidence: 73%

Section: Simulation Results and Comparison With Other Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Spectral Vacancies Prediction Method for Cognitive Radio Applications

Canavitsas

Mello

Grivet

2016

J. Microw. Optoelectron. Electromagn. Appl.

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“…Hence, with efficient usage of spectrum creating opportunities, CR networks allow the secondaryusers to allot spectrum slots for their communication and leave it on the primary user [32]. The improvised spectrum management scheme has been introduced by Akhtar et al [33], which illustrated spectrum utilization policies for cognitive radio networks and contained four significant components, namely, spectrum-sensing, decision, sharing, and mobility framework. The spectrum sharing illustrates how secondary users can select the channel for access coordination.…”

Section: Spectrum Management In 5g Networkmentioning

confidence: 99%

Survey: 5g Wireless Communication Using Cognitive Radio

Kursheed¹,

Budyal²

2020

INDJCSE

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With the increasing growth in the wireless communication systems, the bandwidth requirement is also continuously increasing, and the increasing demands make the wireless communication resources more and more scarce. Cognitive radio technology facilitates an effective solution for bandwidth scarcity through a process of dynamic and opportunistic spectrum sharing over heterogeneous wireless networks. However, the fundamental goal of the 5G communication system based network is to offer extended coverage, a very high data transfer rate, massive connectivity with minimum latency as performance metrics of 5G communication system. To achieve these requirements, the 5G systems exploit the spectrum range from 30Ghz to 300GHz. This paper presents various aspects of theoretical background that relate the research insights of cognitive radio technology, spectrum sharing, and 5G communication system. The inferring of the research evolution and trends in the domain of the collaborative study of spectrum sensing, cognitive radio, and 5G provides future research directions based on the research gap analysis

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“…Therefore, for efficient utilization of the spectrum creating opportunities, Cognitive Radio (CR) technology allows its users called Secondary Users (SUs) to occupy the available spectrum slot for their communication (model is shown in Figure 2) and vacate it on arrival of the licensed user (called Primary User (PU)) [18]. The process of spectrum utilization using CR systems requires a Dynamic Spectrum Management Framework (DSMF) [19] which consists of four main components (naming Spectrum Sensing (SS), Spectrum Decision Framework, spectrum sharing and spectrum mobility). Spectrum sharing refers to coordinated access to the selected channel by the SUs.…”

Section: Need For Spectrum Management In Iot Based 5g/b5g Networkmentioning

confidence: 99%

Spectrum Decision Framework to Support Cognitive Radio Based IoT in 5G

Akhtar¹,

Arif²,

Siddique³

2018

Cognitive Radio in 4g/5g Wireless Communication Systems

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RF Spectrum Decision in Cognitive Radio enables unlicensed users of wireless communication systems to occupy the vacant spectrum slots as a solution to scarce spectrum. Internet of Things (IoT) is a wide-reaching network of unified entities. IoT capable things will be interconnected through wireless communication technologies offering cost-effectiveness and accessibility to remote users making quality life style. IoT implementation suffers from challenges of vulnerabilities to dynamic environmental conditions, ease of access, bandwidth allocation and utilization, and cost to purchase RF spectrum. As RF spectrum is a precious commodity and there is a dearth of RF spectrum, hence IoT connections are drifting towards Cognitive Radio Networks (CRNs). Permeating things with cognitive abilities will be able to make RF spectrum decisions to achieve interference-free and wireless connectivity as per their QoS requirements. The wireless systems are rapidly advancing. The leap from packet switching along with circuit switching with 144 kbps data rate (2G and 2.5G) to Long Term Evolution Advanced (LTE-A), i.e., 4G occurred in one decade time frame. As the current wireless connectivity is aimed at higher capacity, higher data rate, low end-to-end latency, massive device connectivity, reduced cost and consistent Quality of Experience (QoE) provision, therefore, 4G is being replaced with 5G. Presently the Radio Frequency (RF) spectrum band is fully sold out and allocated to various wireless operators and applications. On the other hand, new wireless applications are emerging and there is a serious dearth of frequency spectrum to be allocated to emerging wireless services. The efficient utilization of assigned RF spectrum which is otherwise underutilized due to the typical usage by the licensed users known as Primary Users (PUs) is the one of the best possible way to implement IoT in 5G. Thus the Spectrum Decision by unlicensed users of CR holds a significance in CR-based IoT in 5G and beyond network. This chapter describes a scientific supported spectrum decision support framework for CR Network. The main goal of this chapter is to discuss how CR technology can be helpful for the IoT paradigm.

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Fusion based spectrum decision framework for cognitive radio users

Cited by 4 publications

References 14 publications

Spectral Vacancies Prediction Method for Cognitive Radio Applications

Spectral Vacancies Prediction Method for Cognitive Radio Applications

Survey: 5g Wireless Communication Using Cognitive Radio

Spectrum Decision Framework to Support Cognitive Radio Based IoT in 5G

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