A communication-aware and predictive list scheduling algorithm for network-on-chip based heterogeneous muti-processor system-on-chip

Yao, Yu; Song, Yong; Ge, Hu; Huang, Yuehui; Zhang, Duoli

doi:10.1016/j.mejo.2022.105367

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…The planning system must use network base station information, terminal test pilot data, terminal geographic location data, etc. in order to take into account the operational objectives of the RPMA large data base station network [14]. The system must first clean the collected data, remove the features that contain default values and a large number of duplicate data, and extract various features that affect the signal coverage quality.…”

Section: Big Data Base Station Traffic Processing Model Integrating C...mentioning

confidence: 99%

“…Therefore, the algorithm needs to focus on the terminals with low coverage quality and greater impact weight is given. According to the above contents, each data point will generate the corresponding weight value w(p i ), and the band formula of the cluster center position can be obtained by combining f b j |p i and w(p i ), see formula (14).…”

Section: Design Of Base Station Deployment Model Based On Weighted K-...mentioning

confidence: 99%

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Traffic Processing Model of Big Data Base Station Based on Hybrid Improved CNN Algorithm and K-Centroids Clustering Algorithm

Xu¹,

Shen³

et al. 2023

IEEE Access

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Wireless communication network (WCN) is very important for providing convenient mobile network communication services. Random Phase Multiple Access (RPMA) WCN under heterogeneous network architecture is widely used in wireless network construction worldwide due to its low power consumption and high cell density. However, this kind of WCN cannot meet the application scenario of high communication quality. Therefore, this research builds an RPMA communication quality prediction model for big data wireless base stations, which combines the convolutional neural network (CNN) algorithm and the lifting regression tree algorithm. It can be used to find the elements that have obvious influence on the communication quality. The reason for choosing the convolutional neural network algorithm is that its nonlinear feature relationship search and processing ability is excellent, and its computational complexity is relatively small. However, it is a black box algorithm and cannot obtain the importance coefficients of each feature, which is not conducive to subsequent analysis. Therefore, it is also necessary to select a relatively simple modified regression tree algorithm to participate in the calculation. The model constructed by integrating convolutional neural network and lifting regression tree algorithm is the RPMA wireless big data base station communication quality prediction model. A base station planning and deployment model based on weighted K-centroids algorithm is designed to obtain a better base station deployment scheme. In the CNN-DT model, the importance coefficients of T_B_diff, P_La and P_Lo are the largest and significantly larger than those of other features, which are 0.352, 0.289 and 0.264 respectively. The weighted K-centroids clustering algorithm designed in this study has the best overall downlink reception signal (RSSI) value distribution. For RSSI bucket ''-140∼-130'', the number of test points of WK centroids model, K-means model, GMC model, Mean Shift model and spectral clustering model accounted for 1.95%, 6.25%, 4.25%, 8.22% and 7.13% respectively. The model constructed in this study based on CNN and improved regression tree algorithm can accurately predict the communication quality of wireless big data base stations. This study's main contribution is that it can be used in conjunction with the base station planning and deployment model based on weighted K-centroids algorithm to improve the accuracy and effectiveness of location selection for the RPMA wireless communication network base station.INDEX TERMS CNN, promote the regression tree, K-centroids, big data base station, WCN.

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Section: Big Data Base Station Traffic Processing Model Integrating C...mentioning

confidence: 99%

Section: Design Of Base Station Deployment Model Based On Weighted K-...mentioning

confidence: 99%

Traffic Processing Model of Big Data Base Station Based on Hybrid Improved CNN Algorithm and K-Centroids Clustering Algorithm

Xu¹,

Shen³

et al. 2023

IEEE Access

View full text Add to dashboard Cite

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“…Multiprocessor interconnection networks are habitually expected to join numerous indistinguishable processor-memory matches that are rehashed everyone is alluded to as a handling hub [16]. Message passing instead of divided memory is much of the time utilized for synchronization of correspondence among the handling hubs for the execution of the program.…”

Section: Introductionmentioning

confidence: 99%

Optimized Evaluation of Mobile Base Station by Modern Topological Invariants

Hamid¹,

Iqbal²,

Ashraf³

et al. 2023

CMC

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Due to a tremendous increase in mobile traffic, mobile operators have started to restructure their networks to offload their traffic. New research directions will lead to fundamental changes in the design of future Fifthgeneration (5G) cellular networks. For the formal reason, the study solves the physical network of the mobile base station for the prediction of the best characteristics to develop an enhanced network with the help of graph theory. Any number that can be uniquely calculated by a graph is known as a graph invariant. During the last two decades, innumerable numerical graph invariants have been portrayed and used for correlation analysis. In any case, no efficient assessment has been embraced to choose, how much these invariants are connected with a network graph. This paper will talk about two unique variations of the hexagonal graph with great capability of forecasting in the field of optimized mobile base station topology in setting with physical networks. Since K-banhatti sombor invariants (KBSO) and Contrharmonic-quadratic invariants (CQIs) are newly introduced and have various expectation characteristics for various variations of hexagonal graphs or networks. As the hexagonal networks are used in mobile base stations in layered, forms called honeycomb. The review settled the topology of a hexagon of two distinct sorts with two invariants KBSO and CQIs and their reduced forms. The deduced outcomes can be utilized for the modeling of mobile cellular networks, multiprocessors interconnections, microchips, chemical compound synthesis and memory interconnection networks. The results find sharp upper bounds and lower bounds of the honeycomb network to utilize the Mobile base station network (MBSN) for the high load of traffic and minimal traffic also.

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Performance evaluation of modified mesh-based NoC architecture

Reddy

Kar

2022

Computers and Electrical Engineering

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A communication-aware and predictive list scheduling algorithm for network-on-chip based heterogeneous muti-processor system-on-chip

Cited by 7 publications

References 19 publications

Traffic Processing Model of Big Data Base Station Based on Hybrid Improved CNN Algorithm and K-Centroids Clustering Algorithm

Traffic Processing Model of Big Data Base Station Based on Hybrid Improved CNN Algorithm and K-Centroids Clustering Algorithm

Optimized Evaluation of Mobile Base Station by Modern Topological Invariants

Performance evaluation of modified mesh-based NoC architecture

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