在自然的電磁傳播環境中，會有很多不利的傳播狀況，例如：造成訊號衰減的多路徑傳播，以及由環境中大型物體造成的折射與反射。因此，環境經常會對通訊效率以及服務品質造成負面的影響。
大型智慧反射表面（Large intelligent surface, LIS）是一種由被動無線元件組成的人工結構，其中每個元件都可以藉由可控制的相位變化量來改變訊號的相位，藉此反射訊號，而這可以使訊號避免因環境中的障礙物造成的失真。因此，大型智慧反射表面被看作是一種大有可為的技術，可以用來為未來的通訊系統建立一個可用程式設計的無線環境，藉此提升服務品質。
在本篇論文中，我們聚焦在由LIS輔助的多基地台上傳系統的最佳化設定。我們
制定了一個加權總傳送速率(weighted sum rate, WSR)的最大化問題，其中，我們將會設計(1) LIS的相位、(2)使用者訊號的強度以及(3) 使用者與基地台的組合。這個問題首先被分解成(1) LIS相位最佳化、(2)使用者訊號最佳化以及(3) 使用者與基地台的組合最佳化三個子問題，而原始問題可以藉由輪流固定其中兩項以及最佳化另一項的方法漸漸逼近最佳解。我們在論文中分析了三個子問題，並且對每個子問題都提出了演算法。在LIS相位最佳化子問題以及使用者訊號最佳化子問題中，我們藉由輪流固定目標變數以及輔助變數逐漸逼近最佳解，在使用者與基地台的組合最佳化子問題中，我們提出了一個greedy演算法，並且證明該演算法能得到最佳的使用者與基地台的組合。
最後，模擬的結果顯示我們的演算法可以比許多不同的方法獲得更好的WSR，
由此可以確定我們的設計對多基地台上傳系統的系統效能有顯著的提升。

In a natural electromagnetic propagation environment, there are many unfavorable propagation conditions such as multipath propagation, and reflections or refractions from large objects. Therefore, the environment usually has a negative effect on the communication
efficiency and the Quality of Service (QoS).
Large intelligent surface (LIS) is a man-made structure which consists of passive radio elements, each of the elements can reflect the incident signal by inducing a manageable phase shift, and this can make the incident signal avoid the distortion caused by objects in the environment. Therefore, LIS has been seen as a promising technology to build a programmable wireless environment for future communication systems to improve the QoS of users.
This thesis focuses on the optimal configuration of LIS-aided multi-user uplink system with multiple base stations. We formulate it as a weighted sum rate maximization problem, which requires a joint design of the base station association, the precoders, and the LIS phase shifts. The problem is divided into the LIS phase shift optimization subproblem, the precoder optimization subproblem, and the base station association optimization subproblem. The original problem can be solved by using alternative optimization with solving the three subproblems. We analyze the three subproblems and propose optimal algorithms for each subproblem. We solve the LIS phase shift optimization subproblem and the precoder optimization subproblem by alternative optimization by solving the objective variables and the auxiliary variables, solving the base station association optimization subproblem by a greedy algorithm, and proving that we can get the optimal association by the greedy algorithm.
Simulation results show that our algorithm achieves a better weighted sum rate than the different benchmarks, which validate that a joint LIS-aware design is critical for such a system. Besides, simulation results also show which part of the optimization algorithm is
the critical part with different numbers of users and phase shifts.

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