為了解決LTE-A異質網路的干擾問題，「第三代合作夥伴計畫」(3rd Generation Partnership Project，簡稱3GPP)於Realse 10版本提出「增強型細胞間干擾消除技術」(enhanced inter-cell interference coordination，簡稱eICIC)，此技術讓macrocell的基地台可以在某些子訊框(subframe)中，降低傳輸功率不傳送使用者資訊，使small cell的基地台可以在幾乎不受干擾的狀況下傳送資料，這些subframe我們稱之為「幾近空白子訊框」(almost blank subframe，簡稱ABS)；然而，當使用ABS時，雖然提升了small cell的吞吐量(throughput)，但相對macrocell會因為無法傳送資訊，而降低其傳輸能力，因此，我們必須適當的調整ABS的比率(ratio)，否則無法有效提升系統效能；儘管已經有許多調整ABS ratio的方法被提出，但大多數僅考慮到UE的傳輸能力或是佇列(queue)中的資料量，沒有將封包的時效性納入考量。
因此，本論文提出「考量QoS的ABS調整機制」(QoS-aware ABS adjustment scheme，簡稱Q-AAS)，其包含四個階段，並可根據使用者的分布、通道狀況、queue中待傳資料量以及即將過期的資料量，於每個ABS週期中動態調整ABS ratio，以便提升整體網路的throughput、保障macrocell的throughput以防止出現飢餓(starvation)狀況，同時降低GBR UE的封包遺失率(packet loss rate)以保障其服務品質(quality of service，簡稱QoS)；根據模擬結果顯示，Q-AAS演算法能根據UE的分布以及網路的流量來適當的調整ABS ratio，有效提升整體網路的有效吞吐量(goodput)、保障macrocell的goodput，並且降低GBR UE的封包遺失率。

In order to solve the problem of interference in long term evolution-advanced (LTE-A) heterogeneous networks, 3GPP organization proposed enhanced inter-cell interference coordination (eICIC) in release 10. This technology allows a macro-cell base station to decrease its transmission power at certain subframes and stop transmitting user data. We call such a subframe an almost blank subframe (ABS). Thus, the small-cell base station can transmit packets to its served users nearly without interference. Although using ABS can improve users’ throughput in small cells, users in the macrocell cannot receive data at that subframe. To improve system performance, we should properly adjust ABS ratio. Many schemes have been proposed to deal with this issue, but most of them adjust ABS ratio based on only user channel quality or the amount of data in queue. None of them considers packet delay.
Therefore, we propose a QoS-aware ABS adjustment scheme (Q-AAS), whose objectives are to improve overall network throughput, avoid macrocell user being starvated, and reduce packet loss rate (PLR) of guaranteed bit rate (GBR) flows. Q-AAS algorithm considers user distribution in the network, users’ channel quality, packet delay budget, and urgent data in the queue. Simulation results show that Q-AAS algorithm can improve network goodput, guarantee the transmission in the macrocell, and decrease GBR packet loss rate.

論文審定書 i
致謝 ii
摘要 iii
Abstract iv
目錄 v
圖次 vii
表次 viii
第一章 導論 1
1.1 前言 1
1.2 LTE-A異質網路與干擾消除技術 3
1.3 研究動機 5
1.4 論文貢獻與章節架構 6
第二章 LTE與eICIC技術之介紹 8
2.1 LTE頻譜資源與資料流分級 8
2.2 異質網路下抗干擾技術 13
第三章 問題定義與相關研究 15
3.1 網路架構與問題定義 15
3.2 相關文獻探討 17
第四章 Q-AAS演算法 20
4.1 階段一：計算SINR 23
4.2 階段二：求出網路狀態參數τ與cell可達成資料量 24
4.3 階段三：分析UE佇列 26
4.4 階段四：調整ABS ratio 28
4.5 Q-AAS演算法設計原由 32
第五章 模擬結果與分析 34
5.1 模擬參數 34
5.2 系統有效吞吐量 38
5.2.1 系統總有效吞吐量 38
5.2.2 Macrocell有效吞吐量 41
5.2.3 Picocell-center有效吞吐量 44
5.2.4 Picocell-edge有效吞吐量 46
5.3 GBR flow封包遺失率 48
5.3.1 網路總封包遺失率 48
5.3.2 Macrocell封包遺失率 50
5.3.3 Picocell封包遺失率 52
第六章 結論與未來研究方向 54
參考文獻 55

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