本篇論文使用結合人工蜂群演算法(Artificial Bee Colony Algorithm,ABC)和選擇性映射(Selecting Mapping,SLM)來提出一種改良型基於選擇性映射技術的人工蜂群算法(Modified Artificial Bee Colony-Based SLM,MABC-SLM)技術來達到改善正交分頻多工(Orthogonal Frequency Division Multiplexing,OFDM)系統中常見的高峰均功率比值(Peak-to-Average Power Ratio,PAPR)問題。ABC 是一種通過模仿蜜蜂行爲所研究出來的一種優化算法,是屬群體智能思想的一種具體應用,其主要特點在於不需要瞭解待解决問題的特定信息,通過對人工蜂群個體解(Individual solution)的優劣進行比較,就可以在群體中將全域最優值凸顯出來。本篇論文將針對基於SLM 的人工蜂群演算法進行改進。模擬結果顯示,推薦方法相較於傳統的應用於SLM 的人工蜂群演算法及可以進一步降低正交分頻多工系統中産生的高峰均功率比值,將其應用在延伸星座點方案(Constellation Extended System,CES)中同樣可以産生良好效果。
In this thesis, a mothed called Modified Artificial Bee Colony-Based SLM (MABC-SLM) combined Artificial Bee colony Algorithm (ABC) with Selective Mapping (SLM) was been proposed to improve the Peak-to-Average Power Ratio (PAPR) problem in Orthogonal Frequency Division Multiplexing Systems (OFDM). The artificial bee colony Algorithm is an optimization Algorithm developed by imitating bee behavior. It is a specific application of group intelligence thinking. Its main feature is that it does not need to know the specific information of the problem to be solved. By comparing the advantages and disadvantages of the individual solution, the global optimal value can be highlighted in the group. This thesis will improve the artificial bee colony Algorithm based on selective mapping technology. The simulation results show that the recommended method is compared with the traditional artificial bee colony Algorithm applied to Selective mapping and can further reduce the peak-to-average power ratio generated in the orthogonal frequency division multiplexing System. Good results can also be achieved in the Constellation Extended System (CES).