多輸入多輸出無線通訊系統能夠改善傳統單一天線系統的吞吐量，並且帶來更好的傳輸可靠度，將在未來無線網路高傳輸資料率與高服務品質的需求下扮演相當重要的角色。然而在多輸入多輸出系統中利用配置多重天線獲得效能增益的同時，卻也無可避免地導致多輸入多輸出系統中包含硬體成本例如射頻鏈路增加以及干擾損害例如多重存取干擾加劇等缺陷。

為了克服上述的缺陷且增加系統的效能，許多可行的技術已經陸續被開發。由於前置編碼能夠補償天線選擇造成的陣列增益損失，另一方面，天線選擇可以彌補進行前置編碼而引起的分集增益損耗，因此結合兩個機制的系統將可以提升整體系統的效能。本論文提出一個簡單而且有效率的基因演算法，或稱作混合式基因演算法，解決在下鏈的有限回授多使用者多輸入多輸出系統中結合考慮傳送天線選擇與量化前置編碼兩獨立機制的問題，期望能在降低硬體複雜度的同時，亦獲得良好的系統效能。接著，更利用混合式基因演算法處理在基地台已知完美通道資訊的情況之下，同時結合傳送天線選擇與最佳前置編碼的問題。最後，我們亦把功率分配納入一起考量，希望可以進一步提升整體效能。本論文提出的新式基因演算法中，每一組染色體分作三個部份: 量化/最佳前置編碼由一個二位元串列表示、傳送天線選擇由一個整數串列表示，而功率分配則由另一個二位元串列所表示。新的交配與突變機制亦將隨著混合式染色體的提出而被使用。

相關的模擬結果顯示吾人提出的方法相較前人提出的方法，可以獲得更好的系統效能，並且大量、有效地降低運算複雜度。

Multiple-input multiple-output (MIMO) systems are one of the potential promises to meet the rapidly growing demand for higher transmission data rates
and better quality of service (QoS) for wireless communications. However, deploying a large number of antennas in MIMO systems also incurs two setbacks. First, the hardware cost will increase. Second, the interference impairment becomes more pronounced.

Many enabling technologies have been developed to alleviate these inevitable setbacks. Since the precoding can compensate for the loss of array gain during antenna selection and the antenna selection can offset the degradation of diversity in the precoding, it is advantageous to combine both techniques to enhance the overall system performance. In this thesis we propose a simple and efficient genetic algorithm-assisted approach for simultaneously selecting the quantized precoding vectors and the transmit antenna subset in the downlink of multi-user MIMO systems with limited feedback. We then extend this GA approach to joint optimal precoding and transmit antenna selection when perfect channel state information (CSI) is available at the base station (BS). The power allocation is taken into consideration as a whole to further enhance the system performance. In the new GA approaches, each chromosome can be divided into a bit string for quantized/optimal precoding, an integer string for transmit antenna selection, and a bit string for power allocation. In addition, new crossover and mutation operations are addresses to accommodate these new chromosomes.

Furnished simulations show that the proposed new GA approaches indeed yield superior performance with reduced computational complexity overhead compared with previous works in various scenarios.

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