隨著無線通訊技術的快速進步，網路系統需求也日以俱增，例如更優質的連線穩定度、更低的通訊延遲以及更遠的傳輸距離。近年來，智慧反射面被視為節點通訊的新興技術，憑藉低成本的優勢，有望取代傳統的中繼設備。智慧反射面由大量的可重構反射元件所組成，這些元件可動態地調整接收到訊號的相位，以繞過遮蔽物並降低傳輸路徑損耗，達到提升整體通訊品質的效果。智慧反射面上的元件可分為傳統的被動式元件與新型的主動式元件，相較於被動式元件，主動式元件可以透過消耗額外的能量以放大接收訊號的振幅，進而降低訊號的衰落。此外，在具有指向性的多天線基地台中，主波瓣效應會導致智慧反射面上的接收能量分布不均，然而，現有研究卻鮮少將此效應納入系統設計時探討。因此，本論文以最大化使用者可達率為目標，將主波瓣效應與混合型智慧反射面納入考量，並在總能量限制的條件下，提出一套優化主動式元件反射係數的演算法，名為功率限制之梯度上升演算法法，由於目標函式為凸優化問題，因此採用梯度上升法結合動態調整學習率，試圖尋求全域最佳解；同時，考量到反射係數為複數值，此演算法引入維廷格導數（Wirtinger Derivatives）計算進行梯度，此外，為符合能量限制，演算法設計中亦透過排序與調整最小影響之元件係數以有效地降低耗能。模擬結果顯示，與傳統方法相比，本論文提出的梯度上升演算法在混合型智慧反射面的環境中可顯著地提升使用者可達率，並維持較高的能源效率。

The rapid advancement of wireless communication technologies leads to the increased demands for network systems, such as the better connection stability, lower communication latency and wider transmission range. In recent years, the emerging Intelligent Reflecting Surface (IRS) has been regarded as a promising technology for communication nodes. Owing to its low cost, IRS can assist conventional relay devices in future wireless networks. An IRS is composed of many reconfigurable reflecting elements that can dynamically adjust the phase of incoming signals. The reconfigurable elements enable IRS to bypass obstacles and reduce path loss, thereby improving overall communication quality. The elements can be categorized into traditional passive elements and newly developed active elements. Compared to passive elements, an active element can amplify the amplitude of its received signal by using additional power then mitigates signal fading. Moreover, in a directional multi-antenna base station, the main lobe effect leads to an uneven distribution of received power across IRS elements. However, existing literature rarely incorporates this effect into system design. Therefore, this thesis aims to maximize user achievable rates by considering both the main lobe effect and hybrid IRS, and proposes the Power-Constrained Gradient Ascent (PCGA) algorithm to adjust the reflection coefficients of active elements under a constraint on total power. Since the objective function forms a convex optimization problem, gradient ascent with adaptive learning rate adjustment is employed to search for the global optimum. Due to the complex-valued reflection coefficients, the PCGA algorithm introduces Wirtinger derivatives for gradient computation. To satisfy the power constraint, the PCGA algorithm also reduces power consumption by sorting and adjusting the reflection coefficients of elements with minimal impact. Simulation results show that the proposed PCGA algorithm in a hybrid IRS environment improves user achievable rates and maintains higher energy efficiency compared to other algorithms.

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