Spectrum handoff in cognitive radio networks: A classification and comprehensive survey

Kumar, Krishan; Prakash, Arun; Tripathi, Rajeev

doi:10.1016/j.jnca.2015.10.008

Cited by 151 publications

(82 citation statements)

References 117 publications

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“…Designing of medium access protocols to increase throughput is considered in previous studies . Few works have considered service time delay and handoff delay as performance metrics while spectrum mobility is considered along with the channel assignment . Some authors have selected energy efficiency as a criteria for channel assignment.…”

Section: Basics Of Channel Assignmentmentioning

confidence: 99%

“…An advantage of this technique is that the possible interference by the PU appearing suddenly and while the SU is still transmitting in that channel can be reduced. However, this scheme may perform poorly in case of wrong prediction as the SU might jump too soon to a new channel thereby causing unnecessary switching and reducing the link utilization. Reactive spectrum Access handoff: According to this scheme, channel switching is only done after interruption by the PU using reactive‐decision spectrum handoff . An advantage here is that no prediction is involved in this method so the chances of false predictions are avoided, but the packets of the ongoing transmission of SU may collide with the packets of PU thereby degrading the throughput. Nonswitching handoff: In this type, the SU prefers staying on the same channel rather than switching .…”

Section: Channel Assignment In Literaturementioning

confidence: 99%

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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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Section: Basics Of Channel Assignmentmentioning

confidence: 99%

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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“…A recent classification and survey was provided by [171]. In [172], a Poisson distribution based model of spectral resources relying on a cross-layer spectrum handoff protocol optimized for the minimum expected transmission time was developed for cognitive LTE networks.…”

Section: Spectrum Inference For Mobilitymentioning

confidence: 99%

Spectrum Inference in Cognitive Radio Networks: Algorithms and Applications

Ding

Jiao

Wang

et al. 2018

IEEE Commun. Surv. Tutorials

159

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Abstract-Spectrum inference, also known as spectrum prediction in the literature, is a promising technique of inferring the occupied/free state of radio spectrum from already known/measured spectrum occupancy statistics by effectively exploiting the inherent correlations among them. In the past few years, spectrum inference has gained increasing attention owing to its wide applications in cognitive radio networks (CRNs), ranging from adaptive spectrum sensing, and predictive spectrum mobility, to dynamic spectrum access and smart topology control, to name just a few. In this paper, we provide a comprehensive survey and tutorial on the recent advances in spectrum inference. Specifically, we first present the preliminaries of spectrum inference, including the sources of spectrum occupancy statistics, the models of spectrum usage, and characterize the predictability of spectrum state evolution. By introducing the taxonomy of spectrum inference from a time-frequency-space perspective, we offer an in-depth tutorial on the existing algorithms. Furthermore, we provide a comparative analysis of various spectrum inference algorithms and discuss the metrics of evaluating the efficiency of spectrum inference. We also portray the various potential applications of spectrum inference in CRNs and beyond, with an outlook to the 5th generation mobile communications (5G) and next generation high frequency (HF) communications systems. Last but not least, we highlight the critical research challenges and open issues ahead.

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“…Equation 10 implies that during the time of t hs , when a CSU can access one idle subchannel, i PUs request the same subchannel. Hence, the CSU has i − 1 successful SHO, and it fails after the ith one.…”

Section: Forced Termination Of the Cognitive Secondary Usersmentioning

confidence: 99%

Modeling and analysis of traffic‐aware spectrum handover schemes in cognitive HetNets

Habibzadeh

Moghaddam

Razavizadeh

et al. 2017

Trans Emerging Tel Tech

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Cognitive radio (CR) heterogeneous network (HetNet) is an emerging technology that aims at enhancing spectrum utilization through spectrum sharing among macro base stations and femto base stations. Similar to other CR networks, in CR HetNets, providing the desired quality of service for the licensed primary users (PUs) is of utmost importance. Due to the shared spectrum between PUs and cognitive secondary users (CSUs) as well as offloading policies, the spectrum handover (SHO) process must be carefully designed. Using the idea of reserved channels, this paper proposes 2 traffic‐aware SHO schemes for cognitive HetNets. In the proposed schemes, each CSU can adopt either a shared‐to‐reserved or a reserved‐to‐shared SHO strategy. The proposed approaches can be implemented in both distributed and centralized manners to maintain a trade‐off between performance and complexity. The performance of the proposed schemes is mathematically analyzed, and closed‐form expressions are derived for the blocking probability of the PUs and the forced termination probability of the CSUs. Numerical results show that the proposed schemes outperform the existing SHO approaches, such as always‐change, always‐stay, and proactive methods, specifically at higher arrival rates of PUs. Moreover, several evaluation metrics, such as effective throughput, energy efficiency, and a proposed combinational utility function, are investigated for both distributed and centralized schemes. The results show that the proposed centralized schemes offer higher performance compared to the distributed schemes.

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Spectrum handoff in cognitive radio networks: A classification and comprehensive survey

Cited by 151 publications

References 117 publications

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

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

Spectrum Inference in Cognitive Radio Networks: Algorithms and Applications

Modeling and analysis of traffic‐aware spectrum handover schemes in cognitive HetNets

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