自組式網路中的無線資源管理：分散式學習與穩當策略

Abstract

由於其在頻譜使用上的彈性，自組式網路被視為滿足不斷增加的行動通訊流量需求的一個重要方案。在自組式網路中，共享頻譜的節點是分散的，必須由個別的節點進行無線資源管理。此外，各個節點的無線資源管理決定會影響彼此的效能，因此我們需要能考慮節點的相互作用的分散式無線資源管理方法。為達此目的，本論文將包括博弈論，信息論，隨機學習在內的多元數學工具，用於無線資源管理問題的建模與解決方案。雖然自組式網路中的當紅議題，如異構網絡和無線感知網路等，已有了深入的研究，我們的工作的新穎性在於基於分散式學習演算法，各節點在資訊有限的條件下，仍具有自組與調整的能力。 本論文首先介紹相關的數學工具，包括賽局理論的基礎知識與隨機學習演算法的簡介。接著是關於無線感知網路的一份文獻探討。隨後，我們提供了四個應用實例。在每個例子中，我們針對一個在分散式網路中可能會遇到的無線資源管理問題，建構賽局理論模型。網絡中的節點被視為具備自主學習能力的自動機，並能藉由個別行為-回報歷史，習得適當的資源管理策略。我們亦透過數值模擬，評估學習過程的收斂性及其性能。

Self-organized network (SoN) has been considered as an important solution to the increasing demand of mobile traffics, due to its flexibility in spectrum access. In SoNs, the nodes sharing the spectrum are located in a distributed manner, and the radio resource management (RRM) must be performed by individual nodes. Moreover, since the RRM decisions of the nodes affect the performance of each other, distributed RRM methods considering the interactions of nodes are desirable for SoNs. To this aim, a diversified class of mathematical tools including game theory, information theory, and stochastic learning are involved in this thesis, for the problem formulation and solution of the RRM in SoNs. While the rising topics of SoNs such as heterogeneous networks and cognitive radio networks (CRNs) have been intensively studied, the novelty of our work lies in the capability of self-organization and adjustment under limited information, based on distributed learning methods. We start our presentation with the underlying mathematics, including game theory fundamentals and an introduction to the stochastic learning algorithm. A survey on CRNs follows. Four application examples are provided afterwards. In each example, game theoretical framework is adopted to formulate an RRM problem we may encounter in distributed networks. The nodes in networks are modeled as self-organized learning automata, which learn proper RRM strategies through individual action-reward history. The convergence of the learning procedure and its performance are evaluated via numerical simulations.