在行動邊緣計算中使用多接取技術被認為是解決行動計算需求快速增長的有效方法，行動用戶透過此技術將延遲敏感的任務卸載到邊緣伺服器進行運算。本論文採用稱為速率拆分的多接取技術來支援多用戶的計算卸載。根據此多接取技術，用戶將母訊號拆成兩個子訊號，再給予不同功耗後疊加上傳；而邊緣伺服器則會根據各用戶的上傳資料量及上傳時間決定運算資源的分配比例。本論文主要利用優化任務卸載比例、伺服器運算資源分配比例、子訊號功耗分配比例及解碼順序來最小化最大的用戶計算延遲。由於問題為非凸優化問題，因此通過推導一系列的封閉表達式來簡化問題，並使用交替優化、KKT 演算法和窮舉法找出最小延時。數值結果顯示在邊緣運算系統中，速率拆分多址接取相較非正交多址接取及分頻多接取可降低邊緣計算用戶的延時，顯示速率拆分多址接取技術對行動邊緣運算算是極具吸引力的多用戶接取方案。

Mobile Edge Computing (MEC) with Multiple Access (MA) is regarded as an promising way to fulfill the increasing computation demand from mobile user equipment (UE). In this work, Rate-Splitting Multiple Access (RSMA) is adopted to transmit the offloading tasks from UE to an MEC server. With RSMA, UE splits their original message to two sub-messages each assigned with different power. Then, MEC server decides the computation resource allocation ratio for each UE according to their offloading task size and transmission time. In order to minimize the worst-case delay among multiple UEs, an optimization problem is formulated with task offloading ratio, computation resource allocation, power allocation, and decoding order into account. Since the optimization problem is non-convex with coupled optimization variables, it can not be solved directly using existing approaches. To tackle this issue, we decompose the optimization problem into several sub-problems and alternately optimize each sub-problem until reaching a converged solution. For the sub-problems of task offloading ratio, closed-form expressions for the optimal solution are derived that greatly reduces solution complexity. Numerical results show that overall delay of the proposed RSMA-enabled MEC system is smaller than that of NOMA-enabled and FDMA-enabled counterparts, indicating the superiority of RSMA in supporting future MEC applications.

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