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المؤلفYao, Haipeng
المؤلفYuan, Xin
المؤلفZhang, Peiying
المؤلفWang, Jingjing
المؤلفJiang, Chunxiao
المؤلفGuizani, Mohsen
تاريخ الإتاحة2020-06-02T08:17:30Z
تاريخ النشر2019
اسم المنشور2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/IWCMC.2019.8766546
معرّف المصادر الموحدhttp://hdl.handle.net/10576/15002
الملخصWith the development of Next-generation Wireless Networks (NWNs), delay-sensitive traffic triggered by mobile applications (such as video stream and online games) will become an important part of the NWNs. With the increasing demand for massive video content transmission and good quality of users' experience, NWNs have to face up to some serious challenges. As a remedy, efficient routing schemes are capable of achieving load balance. In this article, we propose a load balance routing based on machine learning. First, a dimension-reduced vector matrix can be obtained from the original adjacency matrix of the network topology by Principal Component Analysis (PCA). Then, a neural network is used for the prediction of the network queue status, which can be used as a metric for making intelligent routing decisions. Finally, a load balance routing algorithm considering Queue Utilization (QU) is designed accordingly. Simulation results show the performance of our proposed machine learning-based routing scheme compared to the shortest path algorithm (Bellman-Ford (BF)) and its variant (QUBF) in terms of the packet loss ratio, the throughput and the delay. - 2019 IEEE.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعLoad balance routing
Machine learning
Next-generation Wireless Networks
Principal component analysis
Queue utilization
العنوانA machine learning approach of load balance routing to support next-generation wireless networks
النوعConference Paper
الصفحات1317-1322
dc.accessType Abstract Only


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