IEEE 802.16網路以支持向量機配置頻寬

Abstract

近幾年無線寬頻網路崛起，寄望WiMAX可以取代最後一哩，雖然WiMAX有QoS的設計，但是對於Call Admission Control、Bandwidth Allocation、Scheduler並沒有實際定義，給予廠商彈性設計。本篇論文提出以機器學習的方式依據網路狀態動態配置頻寬，以符合實際頻寬需求。\r\n 由於BS在配置頻寬的時候並沒有SS佇列的訊息，使得BS無法配置適合的頻寬，達到較好的效能，尤其是有期限的rtPS封包最為明顯。在系統負載較高的環境下，容易導致封包遺失提升，吞吐量降低的情形發生。因此本研究提出了支持向量機的方式，收集大量Training Data，訓練成動態頻寬配置模組；以動態配置適合的頻寬給rtPS，使rtPS在負載高的環境下的封包遺失率降低，且延遲能夠維持一定水準。搭配適應性頻寬配置策略，在低負載的環境下可以保留少許頻寬給Non Real Time Traffic，在高負載環境下，先滿足Real Time Traffic為原則。模擬工具採用NS 2-2.29、長庚大學-資策會的WiMAX模組，以及台大林智仁老師開發的支持向量機函式庫libSVM。

In recent years, the rise of wireless broadband access networks. Hope that WiMAX can solve the last mile problem. Although WiMAX has QoS design, but for call admission control, bandwidth allocation, scheduler are not defined in standard. In this paper, we proposed a machine learning approach dynamic bandwidth allocation based on network state. \r\nBS because of the bandwidth allocation at a time when there is no message of SS’s queue. Enables BS can not configure a more suitable bandwidth to achieve better performance. In particular, there is the deadline of rtPS packets. At the higher loading on the system environment, easily lead to packet loss raise, lower throughput situations happen. In this study, a support vector machine approach to collect a large number of training data. Training modules into a dynamic bandwidth allocation. We can dynamically allocate bandwidth to fit rtPS. Adaptive bandwidth allocation strategy, at the low loading environment can keep some bandwidth for non real time traffic. At a high loading environment must first meet the real time traffic. We use Network Simulator 2-2.29, CGU-III WiMAX module, libSVM library.