次世代蜂巢式網路霧端資源優化

Abstract

霧端運算是一種源自於雲端運算演化，將運算從雲端延伸至邊緣以因應次世 代蜂巢式網路即將興起具有低延遲服務需求之前瞻性最佳解決手段。然而，如何睿智地管理擁有多種不同型態之霧端資源以同時全面考量 1)運算及無線通訊 2)無線電力及通訊則是一個深具挑戰同時並不直覺的問題，因此正是本博士論文的核心探討主軸。從第一種霧端資源最佳化觀點出發，首先針對運算能力匱乏卻擁有服務時間要求限制的物聯網裝置，我們提出了霧端無線存取網路架構，藉由多重霧群同時考量邊緣運算及近端通訊以解決在時間維度上通訊與運算之取捨問題來達成極低延遲時間服務。此外，我們設計一套追求低延遲霧合作群演算法，藉由動態規劃解法依序完成 1)綜觀最佳化主霧節點挑選機制及 2)我為人人策略之霧節點群選定機制以針對多重霧群提供最合適的異質霧端資源配置。最後，在方法分析上顯示出霧端無線存取網路透過追求低延遲霧合作群演算法可以順利達成低延遲服務目標。下一階段考量到另外一種霧端資源最佳化觀點，接著針對能源有限的物聯網裝置去執行近端運算或將運算工作量分擔至鄰近霧節點群，我們推崇可充電式無線霧端網路，其中每個霧節點各自搭載切換波束天線則能同時提供資料傳輸及環境感知無線充電，但多重霧節點群則需面對資料傳輸服務率與能源擷取功率之取捨問題。因此，我們提出了擁有最佳方法表現比值之多項式時間近似演算法。最後，從資料分析結果中明確呈現能源擷取服務透過環境感知無線電暨協同式能源及資料傳輸率波束賦形能夠順利被實現。

Fog computing, evolves from the cloud and migrates the computing to the edge, is a promising solution to meet the increasing demand for ultra-low latency services in next generation cellular networks. However, how to wisely manage various types of Fog resources regarding 1) joint computing and wireless communication and 2) joint wireless power and communication is a challenging and non-trivial problem as our major focus in this dissertation. For computing-limited IoT devices with time-intensive requirement from the first type of Fog resource optimization viewpoint, we propose a Fog Radio Access Network (F-RAN) framework to achieve the ultra-low latency by joint edge computing and near-range communications across multiple Fog groups but occurring a tradeoff between communication and computing in the time domain. Therefore, we propose a latency-driven cooperative Fog algorithm with dynamic programming solution for 1) globally optimized master F-RAN node selection and 2) simultaneous selection of the F-RAN nodes to serve proper heterogeneous Fog resource allocation for multi-Fog groups by one-for-all concept. The performance evaluations show that the low latency services can be accomplished by F-RAN via latency-driven Fog cooperation approach. Considering another type of Fog resource optimization for energy-limited IoT devices executing local computing or offloading their computing tasks to the neighboring Fog nodes, we advocate wireless powered Fog networks, in which Fog nodes with switched beam antennas can jointly provide data communication and ambient wireless power provision but facing a tradeoff between data service rate and harvested power across multiple Fog nodes. Thus, we propose a polynomial time approximation algorithm with the tightest performance ratio. The numerical results show that the energy harvesting services can be achieved by ambient radio frequency collaborative energy and rate beamforming.