下一代異質性網路智能技術之研究

Abstract

在今日的異質性網路中包含了許多的大、小型基站，然而目前這些基站的配置無法滿足未來的使用者需求，實際上在對來的預測當中，至少在2020年的時候需要比今日還要增加100倍的網路容量，才有辦法應付各方面的需求，因此網路的供應商和營運商們，仔細思考著他們手邊所能夠使用的方法，來提升網路的容量。在這場即將到來的戰役之中，有三個面向是值得來思考的：增加網路的密度、使用更多的頻譜和增加頻譜使用效率的各種技術。在本論文當中，我們針對在增加網路密度所遭遇到的各種可能情況來研究，增加網路的密度會採用大量的小型基站，像是家用基站、微型基站和轉傳節點。本論文主要分為兩個部份來探討：首先是關於在合作式通訊當中，轉傳節點選擇的問題，第二個主題是在異質性網路當中干擾管理的問題，關於第一個主題，我們提出了一個完全分散式的演算法稱為「分散式學習為基礎的轉傳選擇」，解決在合作式通訊中的轉傳選擇問題，另一方面在第二個主題當中，我們提出一個稱為「多調子訊框」方案，來減輕在異質性網路當中的干擾問題。

Today's heterogeneous networks comprised of mostly macrocells and small cells will not be able to meet the upcoming traffic demands. Indeed, it is forecasted that at least a 100$ imes$ network capacity increase will be required to meet the traffic demands in 2020. As a result, vendors and operators are now looking at using every tool at hand to improve network capacity. In this epic campaign, three paradigms are noteworthy, i.e., network densification, the use of of higher frequency bands, and spectral efficiency enhancement technique. In this dissertation, we focus on the issue on network densification, which contains many small cells in the network such as femtocells, picocells and relay nodes. The dissertation can be divided into two parts: the first one is about relay node selection in cooperative communication, and the other is about interference management in heterogeneous networks. We proposed an fully decentralized algorithm call 'Decentralized Learning based Relay Assignment' algorithm to solve the relay assignment problem in cooperative communication. On the other hand, in the topic about interference management, we propose an approach called 'Multi-Tone Subframes' to mitigate the interference in heterogeneous networks.