LTE網路中機械通訊隨機存取的動態資源分配

Abstract

世界正蓬勃發展的物聯網，將使得機械通訊的聯網裝置大增，在次世代的無線網路系統LTE 中，這些裝置的連網需求有極高可能將會造成隨機存取的碰撞。在這篇論文中，為了解決這個問題並提高LTE 連網效能，我們提出了連線數量的估計方法，並以此為基礎分析兩個不同的情境，第一個為考慮機械通訊與人對人通訊的共存情形下之賽局理論分析，另一個則為海量接取下的動態資源分配系統Context-Aware Dynamic Resource Allocation (CADRA)。CADRA 的設計概念是先估計連線數量，再適當分配資源，在穩定成功率下追求最高效率，這個方法也可以進一步套用於多種異質性應用下的情境。我們並透過模擬方式驗證理論，並和標準文件中的解決方案做比較，驗證它的可行性。

According to the prediction, the devices in the Internet of Things will grow explosively. While the huge number of machine-to-machine (M2M) devices connects to the LTE system, the bursty random access attempts from them are potential to cause severe random access collisions. To resolve the problem and improve the performance of LTE RACH, this work gives a map on estimating the number of connections. After that, two scenarios for network access are analyzed. One is a game theoretic analysis to a hybrid scenario, and the other is a context-aware dynamic resource allocation (CADRA) scheme designed for massive devices. CADRA is a two-phased method to resolve random access contention. Phase I is for the estimation of random access attempts, and phase II is for the resource allocation in pursuit of high resource efficiency given high success probability. This approach is also competent in diverse applications with different delay constraint. We evaluate the performance and find that our proposed scheme has better performance over other contention solutions in terms of success probability, resource efficiency and delay.