馬爾克夫通道上輪詢通訊協定的最佳化

Abstract

隨著無線區域網路快速發展，新的應用不斷出現。在能源消耗方面,服務品質方面,硬體成本方面都已經有相當的突破。然而為了要商業化，整合性的探討是必要的。也就是說，如何同時討論這些特性以及它們之間彼此的影響是必要的。 我們所提出的解決方案基於這樣的一個概念:報酬!任何有價值的東西我們都可以用這樣東西的報酬來描述。語音的品質我們可以用報酬來描述;無線資源的使用效率我們可以用報酬來描述;即使是服務提供者也可以用報酬來描述。我們基於無線通道符合馬爾克夫模型的假設，運用動態程式技巧試圖找尋最佳化的報酬，這是我們將會在以下文章中討論的。 我們先建立起一個馬爾克夫的模型，在這個模型上我們加上了報酬的觀點。之後我們利用馬爾克夫決策的技巧幫助我們尋找最大的報酬。接著我們考慮在現實封包會遺失的情形下，如何決定重傳封包的方式，以期達到最高的報酬。最後我們將會描述如何在一個實際的系統中，實作我們的方式。

Coming prevalence of wireless LAN invokes the aspiration for adequate and lucrative applications over it. Prevalent researches have been done about energy consumption of mobile stations, implementation of quality of service, architecture modification and cost-down of devices, etc. However, in order to commercialize usage of wireless LAN, integration of all these fields is important. In other words, how to balance so many tradeoffs, simultaneously, is of concern. Could there be any easy way to take into account all these tradeoffs at one time? One solution is to combine all of the attributes into a notion called reward. All things with value, either to operators or to users, can be attributed to reward. Quality of the voice can be described as reward. Efficiency of wireless resource can be described as reward. Income of the service provider, too, can be described as reward! On the assumption of certain wireless channel model, which at this moment we have is a two-state Markovian one, we can appeal to dynamic programming to find the best way to maximize our reward. We start by deriving a Markov model with rewards and the retransmission as the action. We then apply the dynamic programming to find the best policy to maximize the rewards in the context of Markov Decision theory. Also, we discuss how our proposal could be implemented in a real system, i.e., real access points and real mobile stations.