Smart City Gnosys
Smart city article details
| Title | 6G Traffic Prediction With A Novel Parallel Convolutional Neural Networks Architecture And Matrix Format Method Integration |
|---|---|
| ID_Doc | 327 |
| Authors | Melgarejo Bolivar R.P.; Kumar S.N.K.; Priya V.A.; Amarendra K.; Rajendiran M.; Mamani E.G.C. |
| Year | 2024 |
| Published | Journal of Machine and Computing, 4, 1 |
| DOI | http://dx.doi.org/10.53759/7669/jmc202404006 |
| Abstract | In the evolving world of wireless communication, sixth generation (6G) networks represent a significant leap forward. Beyond its high-speed and reliable communication, 6G integrates Artificial Intelligence (AI), making networks intelligent entities. This elevates the infrastructure of smart cities and other ecosystems. A critical factor in 6G's success is real-time traffic analysis. As 6G aims to interconnect billions of devices, it faces unprecedented traffic patterns. Practical traffic analysis ensures optimal performance, resource distribution, and energy efficiency. It also supports the network in handling vital sectors like healthcare and transportation by anticipating congestion and prioritizing crucial data. However, traditional traffic analysis techniques designed for earlier generations cannot accommodate 6G's demands. With 6G's integration of diverse technologies, understanding traffic becomes more challenging. Recent advancements have incorporated deep learning architectures, notably Convolutional Neural Networks (CNNs), for traffic analysis. While these models show potential, adapting them to 6G's specifics remains challenging. This research presents a unique parallel CNN architecture for 6G traffic prediction. It converts network data into an image using the Matrix Format Method (MFM), making it suitable for CNN processing. This innovation addresses the limitations of traditional methods and meets 6G's requirements. Compared to other models, our parallel CNN architecture highlights enhanced performance, promising increased traffic prediction accuracy. It also paves the way for improved resource allocation, energy management, and quality of service in 6G environments. ©2024 The Authors. Published by AnaPub Publications. |
| Author Keywords | 6G; Accuracy; CNN; Machine Learning; Network Traffic Analysis; Wireless Communication |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
23603  | 0.876 | Liang X. | Enhanced Convolutional Neural Network For Image Processing In 6G Smart City Environments | Wireless Personal Communications (2024) |
| 21127 | 0.873 | Wu B.; Zhang J.; Yuan J.; Zeng Y.; Zhan P.; Yin Y.; Wan J.; Gao H. | Dt-Ctfp: 6G-Enabled Digital Twin Collaborative Traffic Flow Prediction | IEEE Transactions on Intelligent Transportation Systems (2025) |
| 26443 | 0.866 | Colares R.A.; Rittner L.; Conforti E.; Mello D.A.A. | Fiber Sensing In The 6G Era: Vision Transformers For Φ-Otdr-Based Road-Traffic Monitoring | Applied Sciences (Switzerland), 15, 6 (2025) |
| 24043 | 0.858 | Horvath K.; Kimovski D.; Kitanov S.; Prodan R. | Enhancing Traffic Safety With Ai And 6G: Latency Requirements And Real-Time Threat Detection | 2025 10th International Conference on Information and Network Technologies, ICINT 2025 (2025) |
| 56431 | 0.858 | Anh V.T.K. | The Rise Of Ai In 6G Networks: A Comprehensive Review Of Opportunities, Challenges, And Applications | International Conference on Advanced Technologies for Communications (2024) |
| 57590 | 0.857 | Prasad Tera S.; Chinthaginjala R.; Pau G.; Hoon Kim T. | Toward 6G: An Overview Of The Next Generation Of Intelligent Network Connectivity | IEEE Access, 13 (2025) |
| 61726 | 0.857 | Santos G.L.; Endo P.T.; Sadok D.; Kelner J. | When 5G Meets Deep Learning: A Systematic Review | Algorithms, 13, 9 (2020) |
| 28775 | 0.857 | Sun M.; Sun L. | Harnessing The Power Of 6G Connectivity For Advanced Big Data Analytics With Deep Learning | Wireless Personal Communications (2024) |
| 59659 | 0.856 | Praywin Sheela A.G.; Vasanthi A.; Joshitha K.L.; Ranjana R. | Unlocking The Potential Of 6G: From Technical Foundations To Practical Applications In Edge Ai | Advances in Computers, 139 (2025) |
| 55969 | 0.855 | Sharma S.; Sharma A.; Van Chien T. | The Intersection Of 6G, Ai/Machine Learning, And Embedded Systems: Pioneering Intelligent Wireless Technologies | The Intersection of 6G, AI/Machine Learning, and Embedded Systems: Pioneering Intelligent Wireless Technologies (2025) |