| 研究生: |
宋騏安 Sung, Chi-An |
|---|---|
| 論文名稱: |
LTE-A網路中D2D通訊系統最佳的模式選擇演算法之研究 Optimal Mode Selection Algorithms for Device-to-Device Communications in LTE-A Networks |
| 指導教授: |
陳曉華
Chen, Hsiao-Hwa |
| 學位類別: |
碩士 Master |
| 系所名稱: |
工學院 - 工程科學系 Department of Engineering Science |
| 論文出版年: | 2017 |
| 畢業學年度: | 105 |
| 語文別: | 英文 |
| 論文頁數: | 190 |
| 中文關鍵詞: | LTE-A 、D2D通訊 、模式選擇 、無線通道理論 、正交頻分多址 、整數規劃 |
| 外文關鍵詞: | LTE-A, Device-to-device communications, Mode selection, Wireless channel, OFDMA, Integer programming |
| 相關次數: | 點閱:96 下載:3 |
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Device-to-device (D2D) 通訊概念的提出是為了在未來有效改善蜂巢網路的負載。本論文回顧過去五年有關於D2D的相關文獻,並且討論了關於D2D通訊優化的相關工作,然後在這些基礎上進行研究。為了有利於讀者的理解,前半部分從行動通訊的基礎概念,無線通訊的擷取技術,到無線通道理論都進行了深入淺出的介紹。後半部分則是根據蜂窩網路的負載形式,提出不同的演算法來來進行模式的選擇。過去大部分的文獻在基於滿載的行動網路進行模式分配時,都是使用較為啟發性的算法,而並沒有直接有效的算法直接將最優的模式分配計算出來。本論文的主要貢獻在於首先證明出在這樣的系統下可以使用線性規劃的算法來解決模式分配問題,而線性規劃的算法是相當成熟且有效。此外,本文另外提出了在不是滿載情況的蜂窩網路下三種D2D模式的選擇(D2D直接通訊、D2D中繼輔助通訊、D2D透過基地台協助通訊),系統對於各個D2D通訊對(pair)進行模式分配的演算法,以期達到最高的系統容量。在三種模式的選擇情況,本文利用了拉格朗日(Lagrangian)乘子法將模式選擇的可行域大幅縮小,進而提升模式分配演算法的效能。
Device-to-device (D2D) communications are proposed to reduce the burden on cellular system. We survey papers related D2D communication in the last five years. Also, we introduced some optimization works in these papers and started this thesis based on their results. In order to make the thesis easy to understand, the first half thesis introduced the basic concept of cellular system, the multiple access techniques, and the wireless channel models. The second half is the main contribution for this thesis. The mode selection problem for D2D communications have been discussed in previous works. However, most of their proposals are heuristic algorithms and none of them proposed efficient algorithms to solved the scenarios discussed in the thesis. We are the first to prove the linear programming can be used to solve this mode selection problem and the answer would not change under the heavy loaded cellular network. Moreover, we proposed the algorithm for relatively light loaded cellular network. Since we can not prove that the linear programming can be used or not, we applied Lagrangian multipliers to narrow down the feasible domain of this problem. However, this scenario can still solved by linear programming relaxation according to the results of simulations. In the nutshell, we can obtain the optimal solution in efficient way for above scenarios.
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