Path Loss and Throughput Estimation Models for an IEEE 802.11af Prototype

Sawada, Hirokazu; Mizutani, Keiichi; Ishizu, Koji; Matsumura, Toshiro; Tran, Ha-Nguyen; Murakami, Homare; Kojima, Fumihide; Harada, Hiroshi

doi:10.1109/vtcspring.2015.7145995

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…The terms DSA and CR are often used interchangeably in the literature. CR functionalities have been standardized in various IEEE protocols, such as IEEE 802.22 WRAN, IEEE 802.11af, and IEEE 1900.x series [35]- [37], enabling DSA. IEEE 802.22 WRAN focuses on the unlicensed use of TV frequency bands to provide wireless broadband access in rural areas without interfering with primary users.…”

Section: Dynamic Spectrum Accessmentioning

confidence: 99%

A Survey on Blockchain for Dynamic Spectrum Sharing

Perera,

Ranaweera,

Kusaladharma

et al. 2024

IEEE Open J. Commun. Soc.

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The rapid increase in mobile users, the IoT, and data-hungry applications have brought forth unprecedented demand on the spectrum, which is scarce; on top of that, the existing static spectrum allocation schemes have resulted in a heavily underutilized spectrum which can be mitigated with a Dynamic Spectrum Access (DSA) scheme with unlicensed users gaining access to the idle spectrum bands of licensed spectrum users opportunistically. Such a DSA and Dynamic Spectrum Management (DSM) scheme would significantly increase spectral efficiency while facilitating new services and applications beyond 5G (B5G) networks. Even with access to new spectrum bands like terahertz (THz) and Visible light communication and enabling technologies such as Software Defined Networks (SDN) and Cognitive Radio (CR), implementing a fully realized DSM requires rapid sensing, coordination, and management, and sharing of idle spectrum bands in a fair manner while preserving the security and privacy aspects, limiting interferences. With their decentralized, immutable nature, blockchains promise the execution of spectrum access and sharing in a fully transparent, fair manner while preserving privacy and security. Furthermore, blockchain-based Smart Contracts (SCs) allow automation of DSM, cryptocurrencies, and tokens to facilitate the trading of spectrum and related resources. In addition to that, blockchains act as an interface for integrating AI and Machine Learning (ML) techniques into DSM, which provides a certain level of intelligence to the underlying architecture. Although several attempts have been carried out to analyze the research gaps in DSM, a comprehensive analysis addressing the blockchains as the primary solution to address DSM has not been carried out. In this survey, we address the potential of a blockchainbased approach toward realizing a decentralized DSM while presenting future directives to improve the use of blockchains for DSM.

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Section: Dynamic Spectrum Accessmentioning

confidence: 99%

A Survey on Blockchain for Dynamic Spectrum Sharing

Perera,

Ranaweera,

Kusaladharma

et al. 2024

IEEE Open J. Commun. Soc.

View full text Add to dashboard Cite

show abstract

“…After that, for p-values corresponding to the same sample size, we compute mean and standard deviation (SD), denoted by pj and s j , respectively (Line 14), and the lower and upper bound for the confidence interval (CI) (Line 15). Assuming that n is the original sample size, if the lower bounds of the confidence intervals for all sample sizes less than n are greater than the critical value α, we are 100(1 − α)% confident that we have enough samples to apply EVT (Line [16][17]. Otherwise, we should collect more data to increase the sample size.…”

Section: Mssd Algorithmmentioning

confidence: 99%

“…Nevertheless, these studies use the existing average statistics-based channel models, which corresponds to the mode extrapolation in the ultra-reliable region. [13], [14], and [16] fit extreme value distribution (EVD) to the whole path-loss or power distribution and then claim that EVD can be used to model fading in wireless channel better than the well-known models. Additionally, in [15] and [17], generalized extreme value (GEV) distribution is used to model the small scale fading at maritime communication, and root-mean-square (RMS) delay spread at V2V communication, respectively.…”

Section: Introductionmentioning

confidence: 99%

Wireless Channel Modeling Based on Extreme Value Theory for Ultra-Reliable Communications

Mehrnia¹,

Coleri²

2020

Preprint

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A key building block in the design of ultra-reliable communication systems is a wireless channel model that captures the statistics of rare events and significant fading dips. Ambitious reliability objectives, on the order of 10 5 − 10 9 packet error rate, only make sense when they are related to a statistical model of the environment in which the system is deployed. In this study, we propose a novel methodology based on extreme value theory (EVT) to statistically model the behavior of extreme events in a wireless channel for ultra-reliable communication. This methodology includes techniques for fitting the lower tail of the distribution to the generalized Pareto distribution, determination of optimum threshold over which the tail statistics are derived, derivation of optimum stopping condition on the sufficient number of samples required to apply EVT, and finally an assessment of the validity of the derived model. The analysis demonstrates that the proposed algorithm decreases the number of required samples for modeling the tail statistics by about 7 × 10 5 , and provides the best fit to the collected data and performs much better than the conventional methods based on the extrapolation of the average statistics in the ultra-reliable regime.

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Modification of Link Speed Estimation Model for IEEE 802.11ac WLANs by Considering Shadowing Effect

Aman

Debnath

2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Path Loss and Throughput Estimation Models for an IEEE 802.11af Prototype

Cited by 8 publications

References 7 publications

A Survey on Blockchain for Dynamic Spectrum Sharing

A Survey on Blockchain for Dynamic Spectrum Sharing

Wireless Channel Modeling Based on Extreme Value Theory for Ultra-Reliable Communications

Modification of Link Speed Estimation Model for IEEE 802.11ac WLANs by Considering Shadowing Effect

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