面對日新月異的科技浪潮，消費性電子產品的普及使得頻譜資源日益吃緊。在行動通訊由5G演進至6G的過程中，傳統正交多重存取(OMA)架構已難以應對龐大的連結負載。因此，具備支援高過載與低延遲傳輸之優勢的非正交多重存取(NOMA)技術備受矚目，特別是採用OFDM結合低互相關複數擴頻碼的多用戶共享存取(MUSA)系統，已成為實現訊號疊加與分離的重要手段。
針對MUSA系統在上行鏈路處於高過載環境下所衍生的訊號偵測難題，本文深入探討並回顧了多種現有偵測技術，包括計算量較低但效能受限的傳統最小均方誤差(MMSE)、改良後的MMSE連續干擾消除(MMSE-SIC)，以及基於差分度量運算以大幅降低複雜度的梯度搜尋演算法(GSA)。在GSA的架構下，除了分析現有的標準搜尋(SS)、交換搜尋(SOC)、跳躍搜尋(JS)與降階搜尋(ROC)等變體外，本研究提出了一種擾動式搜尋方法。此機制的核心在於當GSA演算法在疊代過程中因差分度量的計算後無法讓使用者當前訊號趨向於最佳解時，主動引入隨機擾動，隨機反轉當前使用者訊號的部分位元狀態，使其搜尋路徑能更進一步逼近全域最佳解，從而達到接近最大概似偵測(ML)的優異效能。
模擬結果顯示，在採用高斯雜訊與平坦衰落的條件與特定展頻碼設定下，本研究提出的擾動式搜尋法與傳統GSA各類演算法相比，在疊代過程中增加了些許運算步驟，但其複雜度仍遠小於最大概似偵測所需的計算量，並且其位元錯誤率(BER)表現有所改善。

In response to the rapid growth of wireless communication services, Internet of Things applications, and massive machine-type communications, traditional Orthogonal Multiple Access schemes have become insufficient for supporting massive simultaneous connections within limited radio resources. Therefore, Non-Orthogonal Multiple Access has become a promising solution for future communication systems. Among various NOMA techniques, Multi-User Shared Access is a code-domain scheme that adopts short complex spreading codes with low cross-correlation, allowing multiple users to share the same time-frequency resources in overloaded uplink scenarios.
This thesis investigates multi-user detection in uplink MUSA systems and focuses on improving detection performance under high-overloading conditions. Conventional detectors, including MMSE and MMSE-SIC, are reviewed and compared with Maximum Likelihood detection. Although MMSE-SIC improves BER performance through successive interference cancellation, it suffers from error propagation and high computational complexity.
To achieve a better balance between detection accuracy and complexity, this thesis applies Gradient Search Algorithms to MUSA detection. The complex-valued system model is transformed into a real-valued bit-level model, and a differential metric is used to evaluate whether bit flipping can reduce detection error. Several GSA-based methods are analyzed, and an iterative bit-flipping Jump Search method is proposed to escape local optima. Simulation results show that the proposed method improves BER performance and approaches ML detection while maintaining much lower complexity than exhaustive search.

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