近年來，有鑑於用戶端日益增加的頻寬需求，新一代無線通訊系統LTE-A（Long Term Evolution-Advanced）提出的載波聚合（Carrier Aggregation）技術已成為廣受矚目的研究議題。此技術讓行動裝置能同時聚合多個分量載波（component carrier），以達到更快的傳輸速率。目前載波聚合的相關研究中，大部分著重在無線資源管理問題，其中包含分量載波的配置與封包的排程。分量載波的配置能有效率地決定各個行動裝置所使用的分量載波；封包的排程則是配置各個行動裝置在分量載波上使用的資源區塊（resource block），並決定其調變與編碼方法。目前相關的研究皆沒考慮已配置的分量載波可再被系統更動的情況，也沒考慮調變與編碼方法的使用限制，因此他們所提出的方法可能會限制無線資源的使用，且不符合LTE-A的硬體設計。在此篇論文中，我們假設排程器能在每個傳輸時間間隔（transmission time interval）重新配置分量載波給行動裝置，並同時考量上述調變與編碼方法的使用限制，公式化下行無線資源分配的問題。由於此問題是屬於NP-hard的難題，因此我們提出一個貪婪演算法解決此問題。此方法主要以最大化系統吞吐量為目標，並盡量維持各行動裝置之間的公平性。此方法保證至少達到最佳解的一半效能。實驗模擬結果顯示，此方法的效能優於其他相關研究提出的方法。

In Long Term Evolution-Advanced (LTE-A) networks, the Carrier Aggregation (CA) technique is incorporated for user equipments (UEs) to simultaneously aggregate multiple component carriers (CCs) for achieving higher transmission rate. Many research works for LTE-A systems with CA configuration have concentrated on the radio resource management (RRM) problem for downlink transmission, including mainly CC assignment and packet scheduling. CC assignment determines which CCs are efficiently assigned to each UE. Packet scheduling is referred to the task of allocating resource blocks (RBs) of CCs as well as modulation and coding schemes (MCSs) to UEs at each transmission time interval (TTI). Most previous studies have not considered that the assigned CCs in each UE can be changed. Furthermore, they also have not considered the MCS constraint, as specified in LTE-A standards. Therefore, their proposed schemes may limit the radio resource usage and are not compatible with LTE-A systems. In this paper, we assume that the scheduler can reassign CCs to each UE at each TTI. Based on this assumption, we formulate the downlink radio resource scheduling problem under the MCS constraint, which is proved to be NP-hard. Then, a novel greedy-based scheme is proposed to address this problem. This scheme aims to maximize the system throughput while maintaining proportional fairness of radio resource allocation among all UEs. We show that this scheme can guarantee at least half of the performance of the optimal solution. Simulation results show that our proposed scheme outperforms the schemes in previous studies.

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