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Parallel Computing with a Bayesian Item Response Model
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作者 Kyriakos Patsias Mona Rahimi +1 位作者 Yanyan Sheng Shahram Rahimi 《American Journal of Computational Mathematics》 2012年第2期65-71,共7页
Item response theory (IRT) is a modern test theory that has been used in various aspects of educational and psychological measurement. The fully Bayesian approach shows promise for estimating IRT models. Given that it... Item response theory (IRT) is a modern test theory that has been used in various aspects of educational and psychological measurement. The fully Bayesian approach shows promise for estimating IRT models. Given that it is computation- ally expensive, the procedure is limited in practical applications. It is hence important to seek ways to reduce the execution time. A suitable solution is the use of high performance computing. This study focuses on the fully Bayesian algorithm for a conventional IRT model so that it can be implemented on a high performance parallel machine. Empirical results suggest that this parallel version of the algorithm achieves a considerable speedup and thus reduces the execution time considerably. 展开更多
关键词 Gibbs Sampling High Performance Computing MESSAGE PASSING Interface TWO-PARAMETER IRT Model
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Addressing the CQI feedback delay in 5G/6G networks via machine learning and evolutionary computing
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作者 Andson Balieiro Kelvin Dias Paulo Guarda 《Intelligent and Converged Networks》 EI 2022年第3期271-281,共11页
5G networks apply adaptive modulation and coding according to the channel condition reported by the user in order to keep the mobile communication quality.However,the delay incurred by the feedback may make the channe... 5G networks apply adaptive modulation and coding according to the channel condition reported by the user in order to keep the mobile communication quality.However,the delay incurred by the feedback may make the channel quality indicator(CQI)obsolete.This paper addresses this issue by proposing two approaches,one based on machine learning and another on evolutionary computing,which considers the user context and signal-to-interference-plus-noise ratio(SINR)besides the delay length to estimate the updated SINR to be mapped into a CQI value.Our proposals are designed to run at the user equipment(UE)side,neither requiring any change in the signalling between the base station(gNB)and UE nor overloading the gNB.They are evaluated in terms of mean squared error by adopting 5G network simulation data and the results show their high accuracy and feasibility to be employed in 5G/6G systems. 展开更多
关键词 channel quality indicator(CQI)feedback delay 5G/6G networks machine learning evolutionary computing
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