Energy efficient data detection with low complexity for an uplink multi-user massive MIMO system

Hasan, Mohammad Kamrul; Hosain, Md. Sarwar; Saha, Tonusree; Islam, Shayla; Paul, Liton Chandra; Khatak, Satish; Alkhassawneh, Hula Mahmoud; Kariri, Elham; Ahmed, Eshtiak; Hassan, Rosilah

doi:10.1016/j.compeleceng.2022.108045

Cited by 6 publications

(2 citation statements)

References 18 publications

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“…Real-world optimization issues in a various applications fields are often NP-hard, and even the development of (meta) heuristic optimization algorithms might necessitate significant computer resources [ 103 ]. The rising availability of powerful computer capabilities will be used to solve complex optimization issues with parallel algorithms in numerous industries, including finance, logistics, production, and design [ 70 ]. A lack of unifying frameworks for parallel optimization across techniques, application sectors, and challenges adds to the heterogeneity [ 37 , 38 ].…”

Section: Shortcomings Of Traditional Parallel Computingmentioning

confidence: 99%

“…The rapid processing of monitoring data and the problem of abnormal detection in real-time are becoming increasingly prominent [ 13 ]. The introduction of cloud computing technology into the power field has essential research significance, but the application in the power industry is still in the exploratory stage, and more in-depth research is needed to put it into power production [ 70 ]. Cloud computing technology has attracted much attention with high performance, but how to use cloud computing technology for large-scale real-time data processing has not been studied [ 71 ].…”

Section: Introductionmentioning

confidence: 99%

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Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations

AL-Jumaili

Muniyandi

Hasan

et al. 2023

Sensors

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Traditional parallel computing for power management systems has prime challenges such as execution time, computational complexity, and efficiency like process time and delays in power system condition monitoring, particularly consumer power consumption, weather data, and power generation for detecting and predicting data mining in the centralized parallel processing and diagnosis. Due to these constraints, data management has become a critical research consideration and bottleneck. To cope with these constraints, cloud computing-based methodologies have been introduced for managing data efficiently in power management systems. This paper reviews the concept of cloud computing architecture that can meet the multi-level real-time requirements to improve monitoring and performance which is designed for different application scenarios for power system monitoring. Then, cloud computing solutions are discussed under the background of big data, and emerging parallel programming models such as Hadoop, Spark, and Storm are briefly described to analyze the advancement, constraints, and innovations. The key performance metrics of cloud computing applications such as core data sampling, modeling, and analyzing the competitiveness of big data was modeled by applying related hypotheses. Finally, it introduces a new design concept with cloud computing and eventually some recommendations focusing on cloud computing infrastructure, and methods for managing real-time big data in the power management system that solve the data mining challenges.

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