Nakagami Distribution Parameters Comparatively Estimated by the Moment and Maximum Likelihood Methods

Artyushenko, V. M.; Volovach, V. I.

doi:10.3103/s875669901903004x

Cited by 12 publications

(7 citation statements)

References 3 publications

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“…On the other hand, Nakagami's m-distribution [23] is a more adaptable statistical model that can replicate both the Rayleigh and the one-sided Gaussian fading scenarios. Furthermore, at certain intervals on the mean rating scale, the Nakagami distribution may serve as a suitable approximation to the log-normal and Rician distributions [16,24,25]. The Nakagami and Rician distributions are a better fit for low signal-to-noise levels (SNRs) than they are for high SNRs.…”

Section: Multipath Channel Modelmentioning

confidence: 99%

“…The Nakagami and Rician distributions are a better fit for low signal-to-noise levels (SNRs) than they are for high SNRs. The Nakagami distribution also gives a better fit to experimental data over a broad range of physical propagation of the channels, and it is more flexible than the log-normal and Rician distributions [16,25,26]. The performance of a system will suffer greatly if fading occurs.…”

Section: Multipath Channel Modelmentioning

confidence: 99%

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Performance Evaluation of 5G New Radio Polar Code over Different Multipath Fading Channel Models

2024

IJIES

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The coding of channels is a core part of any communication network. Future wireless systems will need high performance codes with low complexity encoding and decoding in order to meet a wide variety of requirements, from running in highly dependable situations with short information messages and low code rates to running in high throughput scenarios with lengthy messages and high code rates. The investigation of polar codes in fading channels has important implications in the context of implementing polar codes in wireless communications. A significant development in the fields of information and coding theory, polar code allows for the flexible adjustment of code rates and block lengths without compromising error correction efficiency. Recently, the Third Generation Partnership Project (3GPP) New Radio (NR) group has supported polar codes to provide channel coding in the physical control channels of the 5G, wireless standard. Channel coding, which exists inside the physical layer, is essential in determining the latency and dependability of a communication system. However, error correction performance diminishes with shorter message durations. The polar encoding scheme can achieve channel capacity because it is based on the practical use of channel polarization operation. In this research, a polar code is included in the proposed system for data transmission over various multipath fading channel models (Nakagami-m, Rayleigh, and Lognormal fading channels) in the presence of AWGN, and in this design, the Successive Cancellation List (SCL) decoding algorithms for polar code are used in order for the suggested NR-polar QAM-OFDM system to perform better than the uncoded QAM-OFDM system. According to Monte Carlo simulations, QAM constellation NR-Polar-COFDM using LLRs computed due to the normal distribution over different types of channel PDFs performs better than the uncoded QAM-OFDM system in terms of bit error rat BER. To further enhance the system performance, CRC bits are used to enhance the efficiency of SCL decoding by improving distance characteristics. The simulation results show that BER is approximately 10-6 at SNR of 1dB in the AWGN channel, and in the multipath fading channel BER approaches 10-6 at SNR of 22.5dB in the Rayleigh and Nakagami-m fading channels, while in the lognormal fading channel at SNR of 14dB for short information block lengths at code rates (1/3) in the uplink and downlink control channels. The findings additionally confirm the superiority of Polar coding in the situation of transmitting information with short block lengths in the AWGN compared to the NR-LDPC-OFDM.

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Section: Multipath Channel Modelmentioning

confidence: 99%

Section: Multipath Channel Modelmentioning

confidence: 99%

Performance Evaluation of 5G New Radio Polar Code over Different Multipath Fading Channel Models

2024

IJIES

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“…The performance of the estimator was evaluated based on the relative posterior risk. The maximum-likelihood estimates for the Nakagami distribution have been compared with other estimators [24]. Recently, the Bayesian method of estimation is used in order to estimate the scale parameter of the Nakagami distribution by using Jeffreys', Extension of Jeffreys', and Quasi priors under three different loss functions [24].…”

Section: Introductionmentioning

confidence: 99%

“…The maximum-likelihood estimates for the Nakagami distribution have been compared with other estimators [24]. Recently, the Bayesian method of estimation is used in order to estimate the scale parameter of the Nakagami distribution by using Jeffreys', Extension of Jeffreys', and Quasi priors under three different loss functions [24]. Some of the distributional properties and reliability characteristics of this distribution are discussed [25].…”

Section: Introductionmentioning

confidence: 99%

Perspective Chapter: A New Bivariate Inverted Nakagami Distribution – Properties and Applications

Ishaq¹,

Usman²,

Suleiman³

et al. 2023

New Trends and Challenges in Open Data

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In this work, a new Bivariate Inverted Nakagami distribution that can be used to model real-world data sets has been investigated. The newly developed bivariate distribution's cumulative distribution function and probability density function are defined. The bivariate distribution derives from the Farlie Gumbel Morgenstern, and the marginal density functions are also determined. Some fundamental estimation techniques, such as maximum likelihood estimation and inference functions for margins, are used to derive the parameters of its estimates. Applications to real-world data sets pertaining to kidney infection diseases and the UEFA Champions' League group stage for the seasons 2004–2005 and 2005–2006 help to assess the efficacy of the proposed distribution.

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“…Reference [23] presented a new derivative-free search method for finding models of acceptable data fit in a multidimensional parameter space and made use of the geometrical constructs known as Voronoi cells to derive the search in the parameter space. Reference [24] described a method for estimating the Nakagami distribution parameters by the moment method in which the distribution moments were replaced by their estimates. In order to trace the varying working parameters, the online estimated techniques were developed to improve the accuracy of model.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Parameter Identification for Forging Machine Using Reinforcement Learning

Zhang

Du²

2021

Processes

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It is a challenge to identify the parameters of a mechanism model under real-time operating conditions disrupted by uncertain disturbances due to the deviation between the design requirement and the operational environment. In this paper, a novel approach based on reinforcement learning is proposed for forging machines to achieve the optimal model parameters by applying the raw data directly instead of observation window. This approach is an online parameter identification algorithm in one period without the need of the labelled samples as training database. It has an excellent ability against unknown distributed disturbances in a dynamic process, especially capable of adapting to a new process without historical data. The effectiveness of the algorithm is demonstrated and validated by a simulation of acquiring the parameter values of a forging machine.

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Nakagami Distribution Parameters Comparatively Estimated by the Moment and Maximum Likelihood Methods

Cited by 12 publications

References 3 publications

Performance Evaluation of 5G New Radio Polar Code over Different Multipath Fading Channel Models

Performance Evaluation of 5G New Radio Polar Code over Different Multipath Fading Channel Models

Perspective Chapter: A New Bivariate Inverted Nakagami Distribution – Properties and Applications

Real-Time Parameter Identification for Forging Machine Using Reinforcement Learning

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