Load Reduction Device Grouping for Massive MTC Applications

NCUIR > College of Electrical Engineering & Computer Science > Graduate Institute of Communication Engineering > Electronic Thesis & Dissertation > Item 987654321/74581

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/74581

Title:	Load Reduction Device Grouping for Massive MTC Applications
Authors:	林家緯;Lin, Chia-Wei
Contributors:	通訊工程學系
Keywords:	機器型態傳輸;分群;共享ID;物聯網;排程
Date:	2017-08-24
Issue Date:	2017-10-27 14:02:39 (UTC+8)
Publisher:	國立中央大學
Abstract:	機器型態傳輸是一個能支援物聯網並可以滿足5G需求的通訊技術。大量的MTC傳輸會導致大量的信令並會造成核心網路和無線電接入網的負擔。在之前的解決方法裡，主要討論的方向為如何有效的在同一個群組分配裝置傳輸的排程。但大部份的文獻裡並沒有討論如果加入了物聯網的應用程式必須完成的時限會有什麼問題。在本篇論文裡將基於一個圖形模型提出一個演算法來最小化所需的分群數並考慮應用程式的時間限制。我們列出了最小畫分群數的問題包含了延遲的限制並將這個問題修改成裝箱問題的一個變化並有隨時變動的分組數字。這個問題可以利用MILP或是BFD演算法來解決。在結果部份我們比較了4種不同的分群方法: 區域分群、特性分群、BFD、MILP。;Machine-Type Communication (MTC), an enabling communication technology, is actively evolving to support Internet of Things (IoT) and has become one of the 5G requirements. Massive MTC connections cause significant signaling overload to both the Radio Access Network (RAN) and Core Network (CN). Grouping devices to share resource has been widely adopted as a major solution to mitigate instantaneous signaling load in the previous works that focused on how to schedule the transmissions for the devices in the same group. However, most of the previous works did not put the execution time constraints for the IoT applications into consideration while they designed the schedule algorithms. In this thesis, considering the execution time constraints, we propose an algorithm based on a graph model to minimize the number of groups required for the devices. We formulate a group number minimizing problem with latency constraint as a variation of the bin packing problem with unique \emph{dynamic bin size} property, which can be solved by specifically designed mixed-integer linear programming (MILP) or a low complexity best fit decreasing (BFD) algorithm. Compared with methods using device location and features as grouping criteria, advantages of the group number minimizing strategy can be clearly observed.
Appears in Collections:	[Graduate Institute of Communication Engineering] Electronic Thesis & Dissertation

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