在多細胞網路中，用戶深受嚴重的細胞間干擾所影響，因此多點協調技術被提出來解決此問題，透過多個基地台之間的協調來減緩細胞間干擾。在多點協調的眾多技術中，聯合傳輸顯得尤為突出，其核心概念是將干擾轉換成有用的訊號，並由多個基地台同時服務同個用戶，此時聯合傳輸的基地台群集大幅影響系統效能，雖然目前關於多點協調技術已有了相當程度的研究，但組成基地台群集的方式，仍然缺乏具理論基礎的結論。在本篇論文中，我們提出了兩種動態群集設計，並在聯合傳輸場景下評估他們的效能，根據接收訊號的不同，我們考量三種聯合傳輸場景，第一種是非同調聯合傳輸，多個基地台發射同個訊號給某一用戶，但這些訊號間彼此存在著相位差。第二種是同調聯合傳輸，用戶收到多個來自不同基地台的相同訊號，而且這些訊號間不存在相位差。最後一種是多天線的聯合傳輸，我們將同調聯合傳輸延伸到多天線系統中，並在基地台端應用了最大比例傳輸之預編碼，來提高系統的效能。為分析並評估我們所提出的群集方法的效能，我們利用隨機幾何的技術分析使用者的連線品質和吞吐量的理論值，最後我們以模擬結果驗證理論值，並與現有的群集方法進行效能比較。

In the multicell networks, user equipments (UEs) suffer from severe inter-cell interference (ICI). Coordinated multipoint (CoMP) technique has been proposed to mitigate ICI by coordinating multiple base stations (BS) to serve UEs. Among numerous implementation choices of CoMP, joint transmission (JT) is attractive because multiple BSs transmit the same signal through coordination that transfers interfering signals into useful ones. Although CoMP has been extensively studied, how to cluster the BSs for JT is critical to system performance but an effective clustering remains open. In this thesis, we investigate the performance of CoMP-JT and proposed two dynamic clustering methods, namely restricted strongest clustering (RSC) and dependent threshold clustering (DTC). Based on the differences in the signal models, we divide JT into three scenarios and discuss them separately. The first scenario is non-coherent JT,
where the UEs receive the same signal from multiple BSs but with different phases. The second scenario is coherent JT. In contrast to non-coherent JT, the UEs receive the same signal from multiple BSs with the same phase in coherent JT. The last scenario is multi-antenna JT. We extent the coherent JT to the multi-antenna system and the maximum ratio transmission (MRT) precoding is applied in the BS site. To evaluate the performance of the two proposed clustering methods, we derive the user rate and coverage probability by using the tools from stochastic geometry. Simulation results
are presented to verify the accuracy of the theoretical analysis and demonstrate the performance improvement of our proposed clustering methods over conventional ones.

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