LTE-Advanced是LTE的進階版本，在LTE-Advanced中，藉由佈建輕量化的eNB也就是中繼節點，中繼技術被發展用來延伸eNB的通訊範圍並藉由佈建輕量化的eNB也就是中繼節點提升整體網路容量。這些年來，網路供應商不斷地研究網路規劃，如何使用最少的佈建成本服務最多的使用者數量，改善網路系統效能並滿足使用者需求，但使用者往往冀望獲得更好的通訊品質。大部分目前有關網路規劃之文獻僅考慮在一個eNB的通訊範圍內佈建單一種中繼節點，在本論文中，我們基於混合式整數線性規劃定義包含多個eNBs及兩種且多個中繼節點之規劃問題，並證明此規劃問題為NP-complete問題，其中，Type 1中繼節點被佈建在eNB涵蓋範圍的中心區域，而Type 1a中繼節點則被佈建在eNB涵蓋範圍的邊緣區域，接著提出三個佈建演算法著手處理一個大規模的LTE-Advanced中繼網路之規劃問題，並在一個佈建案例與模擬結果中分析所有的演算法，本論文的最終目標為最大化使用者通訊品質和佈建成本之比值，本論文設計的目標函數為使用者的訊號干擾加噪聲值和佈建成本之性價比值。結果顯示，本論文提出的樹與Type 1a中繼節點演算法可提供可觀的服務使用者數，且最小化規劃結果之佈建成本，並獲得最高的訊噪干擾值和佈建成本之性價比，但樹與Type 1中繼節點演算法並未著手處理Type 1中繼節點的佈建問題，因此，樹與Type 1及Type 1a中繼節點演算法被提出，同時佈建典型1與典型1a兩種中繼節點，但此演算法佈建之eNB恐因相距過近且使用相同頻率而造成彼此訊號干擾，因此本論文進而提出干擾協調演算法，此演算法不僅提供使用者最高的平均干擾加噪聲比，且使用較低的佈建成本，此外，相較於樹與Type 1a中繼節點演算法以及樹與Type 1及Type 1a中繼節點演算法，干擾協調演算法可提供服務給更多的使用者且擁有最高的訊號干擾加噪聲值和佈建成本之性價比值，此演算法可帶給使用者最好的訊號品質，最重要的是，提出的干擾協調演算法可為下一代行動通訊網路提供最好的服務通訊品質。;The Long Term Evolution-Advanced (LTE-Advanced) is the further version of Long Term Evolution. In LTE-Advanced, relay technique is developed to extend the communication coverage of evolved Node B (eNB) and to increase network capacity by deploying the lightweight eNB as well as relay nodes (RNs). Network providers have investigated network planning for improving system performance and satisfying user requirements with minimal construction cost and most served users for many years, but users always expect to acquire better communication quality. Most existing literatures on network planning in LTE-Advanced merely considered deploying one kind of RNs within an eNB’s coverage. In this thesis, the planning problem of multiple eNBs and two types of RNs is formulated based on mixed integer linear programming and proved as an NP-complete problem. Type 1 RNs are aimed to place in the center of eNB, and Type 1a RNs are deployed at the cell edge of eNB. Three algorithms are proposed to tackle the planning problem of a large-scale LTE-Advanced relay network and analyzed in a planning case and simulation-based results. The goal of the thesis is to maximize the communication quality of all served users with a lower construction cost. The objective function of the thesis is evaluated with the designed cost performance index (CPI) ratio of average Signal-to-Interference-plus-Noise Ratio (SINR) to construction cost. Results show that the proposed Tree with Type 1a RN algorithm minimizes the construction cost of planning result with a significant number of served users, and yields the highest CPI ratio of number of served users over construction costs. However, the Tree with Type 1a RN algorithm does not tackle the placement problem of Type 1 RN. The Tree with Type 1 RN and Type 1a RN algorithm is further proposed to accomplish the deployment work of multiple eNB, Type 1a RNs and Type 1 RNs at the same time. However, it may construct two eNBs that their distance is too close to cause communication interference from the overlapping area with the same carrier. Therefore, the Interference Coordination algorithm is proposed to prevent deploying eNBs with signal interference. The proposed Interference Coordination algorithm not only eliminates the communication interference as well as the highest average SINR value of all served users but also spends the lower construction cost on network planning. In addition, it serves more users than the proposed Tree with Type 1a algorithm and the Tree with Type 1 RN and Type 1a RN algorithm. Furthermore, it yields the highest CPI ratio of average SINR value to construction cost. The proposed Interference Coordination algorithm is convinced that it provides all served users with the best signal quality. Most importantly, it is expected to provide the best communication quality for next generation mobile networks.
