在這篇論文中，我們介紹無線感知雲端網路的概念，並且對其發展可 能性及架構做了初步發想與介紹，其中我們針對在無線感知雲端上頻譜管 理進行研究，希望藉由地區的平行化，加速整體在雲端上的運作速度，我 們在本篇論文中，探討各種分群的可能(最佳化分群、最大代價分群及平衡 負載分群)，提出了理論證明、時間複雜度分析及模擬數據結果。對於分群 過後因為資訊缺乏的原因，可能導致邊界無線感知網路基地台的彼此干擾 的問題，我們進一步的提出解決方案，將邊界無線感知網路基地台做初步 的頻譜資源分配，本篇論文提出兩種頻譜資源分配方法(最佳化資源分配，
啟發性的資源分配)以理論證明其最佳性，以時間複雜度及模擬數據結果分 析並驗證其可行性。
在未來，我們將著手於實做無線感知雲端網路，將分群法及邊界的資 源分配演算法實做於真實的雲端平台，並結合功率配置來完整實做頻譜管 理運算單元於雲端上，更進一步的在雲端上結合頻譜感測及換手管理，期 望將無線感知雲端網路的概念實做於生活之中，為未來的生活型態提出新 的可能。
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附錄一、工作量產生方式(Loading Generation)
Power allocation engine loading : M : Number of channels
L : Number of APs k : Number of users
(A) Optimal power allocation : If k < ML, f(1)=1, f(2)=2
f(k) = 1 + C2kf(k − 2) + C3kf(k − 3) + ⋯ + Ck−1k f(1) + 1 Computation complexity = f(k)*O(L3)
(B) Suboptimal power allocation : If k ≤ ML,
Computation complexity = (ML + (k − 1)(ML)2−12ML(k2− k)) ∗ O(L3)
詳細內容請參閱 C.-C. Cheng, “Feasibility Assessment for A Cloud-Based Cognitive Radio Mobile Network - A Power Perspective,” Master thesis, Institute of Communications Engineering, National Chaio Tung University, 2012.