多輸入多輸出(multiple-input multiple-output, MIMO)無線通訊系統能大幅改善頻譜效率(spectral efficiency)且帶來更好傳輸品質，將在未來無線網路的高服務品質(quality of service, QoS)需求下扮演重要的角色。然而多天線的佈置，無可避免地導致多輸入多輸出系統包括硬體成本如射頻鏈(RF chain)增加以及干擾損害如多重串流干擾(multiple stream interference, MSI) 加劇等缺陷。

為有效地克服上述缺陷，本論文首先提出一簡單且更具效率的方法, 此一構想建立在上鏈多用戶多輸入多輸出系統中基於最大概似(maximum likelihood, ML)準則下將接收天線選擇(antenna selection)與訊號偵測(symbol detection)兩個獨立的機制結合考慮，以期能降低硬體複雜度且同時獲得更好的系統效能。為減緩計算上的負擔，吾人也利用改良版基因演算法(genetic algorithm,GA)來輔助求解所提問題。有鑒於天線選擇只挑選出部份最適合的天線，難免會導致系統效能的損失， 為彌補此一缺陷，吾人將探討一種新穎的機制稱為廣義天線組合(generalized antenna combination, GAC)，而此天線組合機制並非僅單純地保留部分天線的資訊，取而代之的是用一轉換陣列對所有天線作線性組合來降低所需的射頻鏈，較之於天線選擇更能有效地保留天線資訊的完整性。再者，本論亦提出基於最小位元錯誤率(mimimum bit error rate, MBER)準則下將天線組合與訊號偵測結合設計，以期達到更好的系統效能與硬體成本的折衷。然而此結合方案也形成了非線性的問題，類似地，基因演算法或微粒群最佳化(particle swarm optimization, PSO)也被用來降低運算複雜度。

相關模擬結果顯示吾人所提之方法較之前人的方法，能獲得更好的系統效能且又大量地降低運算複雜度。

關鍵字：訊號偵測、天線選擇、基因演算法、異質性交配、廣義天線組合、最小化位元錯誤率、多輸入多輸出。

Multiple-input multiple-output (MIMO) systems, which process several merits such as spectral efficiency and better transmission quality, are one of the promising breakthroughs to meet the rapidly growing demand of quality of service (QoS) in the wireless communication systems. However, the performance gains come at the price of expensive hardware cost such as the RF chains associated with the antennas and the stronger interference impairment such as
multiple stream interference (MSI).

To alleviate the above drawbacks, we first present a simple, yet effective approach for joint receive antenna selection and symbol detection based on the maximum likelihood (ML) criterion in the uplink multiuser MIMO systems in order to simultaneously lower the hardware complexity and achieve superior performance. To mitigate the computational overhead, a variant of the conventional genetic algorithm (GA) is employed to solve the above joint problem. Since only part of the antenna is employed, the antenna selection scheme inevitably suffers performance degradation. To overcome this setback, a novel joint generalized scheme of antenna combination (GAC) and symbol detection based on the minimum bit error rate (MBER) criterion is also considered. More specifically, the new approach aims at effectively alleviating the MSI and lowering the RF chains called for by simultaneously determining the antenna combination weighting and the MBER detectors. This joint problem, however, is highly nonlinear and again the GA-based approach or
the particle swarm optimization (PSO)-based approach is employed to reduce the computational overhead.

Relevant simulation results show that both of the proposed approaches yield satisfactory performance with reduced computational complexity overhead compared with pervious works in various scenarios.

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