分散式網路(distributed networks)架構下之適應性傳輸端波束成型技術(adaptive distributed beamforming)的相關研究在近期已有逐漸增多的趨勢，然而相關研究中所考量的系統架構，多半為可取得無雜訊影響接收訊號強度量測值(noise-free received signal strength measurement)且靜態(static)的環境，與實際上分散式網路存在諸多系統變因(system uncertainties)的特性有所差異。有鑒於此，在本研究中，我們將現有的研究成果拓展至包含雜訊影響的環境下，並提出一針對該技術的收斂行為之分析架構(convergence analysis framework)。除此之外，對於隨時變衰退通道(time-varying fading channel)與隨時變之網路拓撲結構(time-varying network topology)的環境，我們則定義了數個關於該技術穩健性之標準量尺(robustness criteria)，可供未來研究者做數值分析的比較用途。最後，透過運用前述之分析架構與量尺，我們提出一仿生機制BioRARSA2，該機制大幅強化適應性波束成型在時變環境與存在未知變因下的穩健性，而相關的性能提升則借助數值模擬做為驗證。

Considerable works on adaptive schemes for transmit beamforming in distributed networks have emerged in the past years, where a noise-free received signal strength (RSS) measurement and a static environment were often considered. In practical environments, however, system uncertainties may rise and the aforementioned ideal assumptions may fail in these settings. Therefore, we focus on robust designs in this thesis and proposed a systematic analytical framework on the convergence of a general set of adaptive schemes under the condition that measurement of RSS at the receiver side is corrupted by noise. In addition, for time-varying channel and time-varying network topology, we defined a set of robustness criteria that can be used as comparison metrics for existing adaptive schemes. By utilizing the proposed analytical frameworks and metrics, we develop an bio-inspired scheme, BioRARSA2, that possess significantly superior robustness with respect to environmental variations and system uncertainties, where the improved robustness is further validated through extensive numerical simulations.

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