Cognitive radio architecture for rapidly deployable heterogeneous wireless networks

Tachwali, Y.; Basma, Fadi; Refai, Hazem H.

doi:10.1109/tce.2010.5606279

Cited by 25 publications

(12 citation statements)

References 12 publications

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“…To make system intelligent the cognitive radio system provides the facilities to sense the spectrum [6] and detect any type of changes either noise, power or energy.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…In spite of all these great features and possibilities, being a new concept, the cognitive radio network poses many new technical challenges. As it is described in the successive sections, these networks have requirements in dynamic spectrum management, complexity and size, power and hardware efficiency, spectrum sensing and interference identification, environment awareness, new distributed algorithm design [6], distributed spectrum measurements, Quality of Service guarantees, user awareness, location awareness and security…”

Section: Introductionmentioning

confidence: 99%

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An Optimized Sensing and Detection of Cognitive Radio Network using Monte Carlo Simulation

Shukla¹,

Gour²

2017

IJCA

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The Cognitive Radio Network is intelligent network, which has the capability to efficiently utilize the available spectrum using various spectrum-sensing techniques, in addition with the intelligent energy consumption and bandwidth allocation. In this paper we are simulating the cognitive radio network using Monte-Carlo simulation model. The proposed system is tested under Additive White Gaussian noise (AWGN) channel and Rayleigh Fading Channel environment. During simulation the probability of detection (Pd) is calculated for given signal to noise ratio (SNR) and false alarm rate (Pf). To enhance the system performance median filter is implemented which significantly enhances the performance of detection probability for given SNR and Pf.

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“…To make system intelligent the cognitive radio system provides the facilities to sense the spectrum [6] and detect any type of changes either noise, power or energy.…”

Section: Proposed Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Optimized Sensing and Detection of Cognitive Radio Network using Monte Carlo Simulation

Shukla¹,

Gour²

2017

IJCA

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“…Another critical reason is the difficulty associated with, and the limiting effects of, considering CRN as an heterogeneous network. The heterogeneity of CRN would imply that in the design of CRN, the wireless network communication infrastructure must be capable of servicing a heterogeneous, probably incompatible, set of wireless consumer devices [34]. One other crucial factor that makes RA problems in CRN very challenging is the limitation in networking and productivity of CRN due to the level of permissible interference to either the PUs, or even among the SUs themselves.…”

Section: Resource Allocation In Cognitive Radio Networkmentioning

confidence: 99%

Solving resource allocation problems in cognitive radio networks: a survey

Awoyemi

Maharaj

Alfa

2016

J Wireless Com Network

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Cognitive radio networks (CRN), in their quest to become the preferred next-generation wireless communication paradigm, will depend heavily on their ability to efficiently manage the limited resources at their disposal in meeting the demands of their numerous users and driving their operations. As a result, a considerable amount of research work has been recently dedicated to investigating and developing resource allocation (RA) models that capture the essentials of CRN. The various ideas put forward by researchers to address RA problems in CRN have been somewhat diverse, and somehow, there seem to be no links that bring cohesion and clarity of purpose and ideas. To address this problem and bridge the gap, in this paper, a comprehensive study on the prevalent techniques developed for addressing RA problems in CRN is carried out, with an intent to put some structure, relevance and meaning to the various solution approaches. The solution models are therefore grouped and/or classified based on certain outstanding criteria, and their strengths and weaknesses highlighted. Open-ended problems are identified, and suggestions for improving solution models are given. The study therefore gives good directions for further investigations on developing RA solutions in CRN.

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“…In [11], the architecture proposed by Haykin emphasizes an awareness of the radio environments to operate in primary spectrum bands. The above two architectures are the most popular, and in addition there are studies on the network architecture of CRNs in the literature focusing on specific research areas [15][16][17][18].…”

Section: Network Architecture Of Crnmentioning

confidence: 99%

Intelligent and efficient development of wireless networks: A review of cognitive radio networks

et al. 2012

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Facing the challenges on how to improve spectrum efficiency and how to realize heterogeneous network convergence in future wireless networks, a cognitive radio network (CRN) is proposed as one of the solutions. This has become a major research topic in recent years and it is timely to give an overview of the development of CRN and to summarize key issues and technologies. The fundamental concepts of CRN, including the cognitive cycle model, the network architecture, and the cognitive ability and intelligent decision functions, are introduced in detail based on recent advances. Key issues for each topic, followed with recent research on theory and method, are then classified and the industrialization developments of CRN testbeds based on TD-LTE cellular system and standards are briefly presented. Finally, conclusions are reached on the perspectives and directions of future development.cognitive radio networks, network architecture, cognitive ability, spectrum sensing, intelligent decision and dynamic spectrum allocation, end-to-end performance Driven by the requirements of ubiquitous wireless access and personalized wireless applications [1], worldwide, numerous wireless technologies are invading our lives, such as evolutionary telecommunication technologies, wideband wireless access and short distance wireless technologies. Furthermore, the concept of the internet of things (IoT) has become widespread based on the development of wireless sensor networks. With the explosive development of wireless technologies, several challenges must be addressed. The electromagnetic spectrum, whose use is licensed and managed by government, has been scarce for allocation to ubiquitous wireless applications. However, contrary to the physical scarcity of the available spectrum, the report published by the federal communication commission (FCC) shows that over 60 % of the licensed spectrum below 6 GHz remains underused [2,3]. The measurement results in Figure 1 taken in Beijing over a one month period, also indicate low spectrum use consistent with the results released by the FCC. Therefore, improvement of spectrum efficiency is the first challenge to be examined. On the other hand, the end-to-end performance of wireless networks is considered to be the performance indicator for ubiquitous and personalized wireless applications [4], such as end-to-end throughput, end-to-end delay and routing scheme, which take into account all network elements in a data transmission flow across different layers for the global optimization from the network aspect. Because the property of heterogeneity causes low radio resource use and mutual interference among heterogeneous networks, the other big challenge is that heterogeneous network convergence is needed to improve the end-to-end performance of wireless networks.Cognitive radio (CR), proposed by Mitola in 1999 [5,6], is considered a feasible solution for improving spectrum efficiency. Furthermore, a cognitive radio network (CRN), which is defined as a wireless network with the capabilities

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Cognitive radio architecture for rapidly deployable heterogeneous wireless networks

Cited by 25 publications

References 12 publications

An Optimized Sensing and Detection of Cognitive Radio Network using Monte Carlo Simulation

An Optimized Sensing and Detection of Cognitive Radio Network using Monte Carlo Simulation

Solving resource allocation problems in cognitive radio networks: a survey

Intelligent and efficient development of wireless networks: A review of cognitive radio networks

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