在本論文中，我們考慮在大規模多輸入多輸出（Massive multiple-input-multiple-output）無線通訊系統中聯合天線選擇與使用者調度(Scheduling)與功率配置的問題，由於Massive MIMO在基地台端配置了大量的天線，使得系統複雜度上升，且需耗費大量的硬體元件像是射頻鏈路，如此系統消耗的功率將大幅增加，因此我們的目標是藉由天線選擇去降低硬體成本，並調度出好的使用者來接收訊號，同時針對功率的部分做配置讓功率能更有效的分配，來達到使系統整體資料傳輸率最大化。此外因近年來節能意識的興起，我們將考量另一種系統衡量標準，即能源效率的問題，其作法是將系統傳輸率除以系統總耗能，評估每單位能量能提供多少的系統傳輸率，為解決以上複雜的問題，我們應用混合基因演算法，有別於傳統基因演算法，此演算法將染色體分為兩部分，前半部分為整數型態，代表天線以及使用者，後半部分為實數型態，代表功率，而整數及實數染色體皆有相對應的混和式交配及突變運算，以此求取系統最佳解，最後模擬結果顯示相對於其他論文，混合基因演算法有更優異的性能。

In this thesis, we consider the problem of joint antenna selection, user scheduling, and power allocation in massive multiple-input-multiple-output (MIMO) wireless communication systems. The massive MIMO systems comprise of a large number of antennas at the base station which increase the complexity and need more hardware components such as RF chains. In recent years, energy efficiency has become a critical design metric for green communication systems. The energy efficiency is defined as the system sum-rate divided by the total power consumption to evaluate the sum-rate per unit energy the systems can provide. In order to maximize the system sum-rate or energy efficiency, we propose a new genetic algorithm to simultaneously perform antenna selection, user scheduling, and power allocation. This algorithm divides the chromosome into two parts. The first part is an integer string, representing the chosen antennas and the user selected. The other part is a real number string, representing the allocated power. In addition, new crossover and mutation operations are employed for this new type of chromosome. Simulation results show that the proposed method can achieve better performance compared with the state-of-the-art methods.

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