An Informative Interpretation of Decision Theory: The Information Theoretic Basis for Signal-to-Noise Ratio and Log Likelihood Ratio

Polcari, John

doi:10.1109/access.2013.2277930

Cited by 9 publications

(4 citation statements)

References 11 publications

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“…Table 5 shows the robustness of the employed model. We employed the Log Likelihood Ratio (LLR) test, as applied by many researchers (Kim et al, 2003;Polcari, 2013aPolcari, , 2013bSur, 2019). The LLR test yielded a Chisquared value of 572.44 and a p value of 0.0000, demonstrating the statistical significance of the model and indicating its superior fit.…”

Section: Resultsmentioning

confidence: 99%

Unveiling determinants of household lighting preferences in rural Tanzania: insights for sustainable energy access

Kamuzora

2024

Sustainable Energy res.

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This paper investigates the determinants and prospects of household lighting choices in rural Tanzania using a Multinomial Logit Regression Model. The analysis is based on data from 4671 households, focusing on three lighting options: electricity, solar energy, and candle lighting. The results reveal significant factors influencing these choices, including household head characteristics, household size, marital status, education, employment status, number of rooms, and income. Key findings indicate that the age of the household head negatively influences the likelihood of choosing grid-electricity, while having a male head of household significantly reduces the probability of opting for any lighting option. Larger household size is negatively associated with choosing electricity and candle lighting. Marital status shows that married households are more likely to use candle lighting. Employment status positively impacts the likelihood of adopting all three lighting options, with employed household heads being more likely to choose modern lighting solutions. Income levels are crucial, as higher income significantly increases the probability of selecting electricity and candle lighting, but not solar energy. These findings provide valuable insights for policymakers and stakeholders aiming to enhance sustainable energy access in rural Tanzania. It highlights the importance of addressing socio-economic factors to promote the adoption of modern and sustainable lighting technologies.

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Section: Resultsmentioning

confidence: 99%

Unveiling determinants of household lighting preferences in rural Tanzania: insights for sustainable energy access

Kamuzora

2024

Sustainable Energy res.

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“…Also,the recovery time involved in failure was said to be improved. In [15] log likelihood ratio was measured for enhanced decision-making in a cloud environment.…”

Section: Related Workmentioning

confidence: 99%

LotkaVoltera and Elman Hebbian Recurrent Neural Network Cache-Based Resource Allocation in Fog Environment

2023

IJIES

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The internet of things (IoT) affects everyday life because digital technology improves. IoT is a group of devices with sensors that talk to each other to reach a goal. IoT systems have traditionally been built on top of cloud computing (CC). IoT devices are slow because CC data centres are separated from them. This slows down the speed at which real-time applications respond. In addition, IoT devices send a lot of data to the cloud to be processed, which overloads the cloud. Edge computing can stop IoT devices from being slow or overloaded. Fog computing (FC) is a way to get services at the edge of a network. With locationawareness, the FC cuts down on latency and overloading. Bandwidth and jitter must be looked at during the process of allocating resources. In this work, the Lotka-Voltera load balancer and elman hebbian-recurrent neural network cache (LV-EHRCC) are proposed for allocating resources in an FC context. LV-EHRCC is made up of load balancing and allocating resources. First, the LotkaVoltera traffic load balancer model is used to increase the amount of Bandwidth available for load balancing. Second, an Elman Hebbian-Recurrent Neural Network model for allocating cached resources efficiently is made for the best load-balanced FC context. Simulations test what will happen. The load balancing capacity of the proposed scheme is 93.25 and attains the highest Bandwidth of 45. In FC simulations, the LV-EHRCC method improves the efficiency of load balancing in terms of Bandwidth, makespan, and jitter rate. The simulation results back up our study and show that LV-EHRCC is better than the benchmark approaches when they are compared.

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“…Next, we directly link the converged log likelihood ratio for the ideal linear channel to the generalized signal-to-noise ratio (GSNR) [20], ref. [21] achieved at convergence, SNR*, which is in turn a function of ψ p(h) (the PDF of the channel gain) [20]:…”

Section: Bussgang Decomposition-based Mpamentioning

confidence: 99%

Comparative Analytical Study of SCMA Detection Methods for PA Nonlinearity Mitigation

Sfeir

Mitra

Kaddoum

et al. 2021

Sensors

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Non-orthogonal multiple access (NOMA) has emerged as a promising technology that allows for multiplexing several users over limited time-frequency resources. Among existing NOMA methods, sparse code multiple access (SCMA) is especially attractive; not only for its coding gain using suitable codebook design methodologies, but also for the guarantee of optimal detection using message passing algorithm (MPA). Despite SCMA’s benefits, the bit error rate (BER) performance of SCMA systems is known to degrade due to nonlinear power amplifiers at the transmitter. To mitigate this degradation, two types of detectors have recently emerged, namely, the Bussgang-based approaches and the reproducing kernel Hilbert space (RKHS)-based approaches. This paper presents analytical results on the error-floor of the Bussgang-based MPA, and compares it with a universally optimal RKHS-based MPA using random Fourier features (RFF). Although the Bussgang-based MPA is computationally simpler, it attains a higher BER floor compared to its RKHS-based counterpart. This error floor and the BER’s performance gap are quantified analytically and validated via computer simulations.

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An Informative Interpretation of Decision Theory: The Information Theoretic Basis for Signal-to-Noise Ratio and Log Likelihood Ratio

Cited by 9 publications

References 11 publications

Unveiling determinants of household lighting preferences in rural Tanzania: insights for sustainable energy access

Unveiling determinants of household lighting preferences in rural Tanzania: insights for sustainable energy access

LotkaVoltera and Elman Hebbian Recurrent Neural Network Cache-Based Resource Allocation in Fog Environment

Comparative Analytical Study of SCMA Detection Methods for PA Nonlinearity Mitigation

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