達成無線網路上下傳的比例公平性
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Date
2010-07-31
Authors
王嘉斌
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Publisher
行政院國家科學委員會
Abstract
在本計畫中，我們研究在IEEE 802.11 無線區域網路(WLAN)中上傳和下傳的公平 性問題。在基礎模式(infrastructure-mode)的無線網路中，資料傳輸分成上傳和下傳；其 中上傳是指資料由無線工作站(mobile stations)傳送至基地台(Access Point，AP)，而下 傳是指資料由基地台傳送至無線工作站。由於802.11 媒體存取控制層(Medium Access Control，MAC)所使用的分散協調式功能(Distributed Coordination Function，DCF)對於 每一個工作站(包括基地台)提供了相同的傳輸機會，將會導致無線網路中上傳和下傳吞 吐量(throughput)的不公平。而這個現象在無線工作站數量增加時將會是更為嚴重的問 題。 在本計畫中我們將會以理論推導的方式分析無線網路中上下傳吞吐量不公平的現 象，並且以此理論模型研究文獻中所提出方法之效能。同時我們也將會提出我們的類 神經網路自適性調整演算法(Neural-Network Based Adaptive Algorithm)，並且比較我們 的方法與文獻中提出方法的效能。我們將提出的演算法是使用類神經網路學習無線網 路系統參數和上下傳吞吐量之間非線性的函數關係，然後將所學到的知識，根據應用 層的服務品質(Quality of Service，QoS)需求調整系統參數，達成公平的上下傳吞吐量比 例。在本計畫中所提出的理論分析以及調整上下傳吞吐量的演算法，希望可作為未來 無線區域網路系統設計之參考。
In this project, we investigate the fairness problem of uplink and downlink throughput of IEEE 802.11 Wireless Local Area Networks (WLAN). In an infrastructure-mode WLAN, the transmissions of data can be classified as uplink transmissions and downlink transmissions. Uplink transmissions refer to a data delivery from a mobile station to the access point (AP), while downlink transmissions refer to that from the AP to a mobile station. Because 802.11 Distributed Coordination Function (DCF) protocol at Medium Access Control (MAC) layer essentially provides equal transmission opportunities to each station include AP with packets to send, unfairness between uplink and downlink throughput is introduced. The unfairness problem will become severer when the number of mobile stations increases. We will deeply investigate the problem of “uplink/downlink unfairness” in WLANs by theoretical analysis, and study the performance of the existing solutions in the literatures. We will furthermore propose our neural-network based adaptive algorithm which performs adaptations on MAC parameters to solve the unfairness problem, and evaluate the performance of our method by comparison with the existing schemes. Our proposed scheme exploits neural networks to learn the nonlinear correlation functions between uplink/downlink throughout and the MAC parameters, and then uses the learned knowledge to adjust MAC parameters to achieve uplink/downlink proportional fairness according to the quality-of-service (QoS) requirements of applications. By both the theoretical analysis and proposed solution for the unfairness problem, we hope to thoroughly provide an in depth view about the system design of IEEE 802.11 WLANs in the future.
In this project, we investigate the fairness problem of uplink and downlink throughput of IEEE 802.11 Wireless Local Area Networks (WLAN). In an infrastructure-mode WLAN, the transmissions of data can be classified as uplink transmissions and downlink transmissions. Uplink transmissions refer to a data delivery from a mobile station to the access point (AP), while downlink transmissions refer to that from the AP to a mobile station. Because 802.11 Distributed Coordination Function (DCF) protocol at Medium Access Control (MAC) layer essentially provides equal transmission opportunities to each station include AP with packets to send, unfairness between uplink and downlink throughput is introduced. The unfairness problem will become severer when the number of mobile stations increases. We will deeply investigate the problem of “uplink/downlink unfairness” in WLANs by theoretical analysis, and study the performance of the existing solutions in the literatures. We will furthermore propose our neural-network based adaptive algorithm which performs adaptations on MAC parameters to solve the unfairness problem, and evaluate the performance of our method by comparison with the existing schemes. Our proposed scheme exploits neural networks to learn the nonlinear correlation functions between uplink/downlink throughout and the MAC parameters, and then uses the learned knowledge to adjust MAC parameters to achieve uplink/downlink proportional fairness according to the quality-of-service (QoS) requirements of applications. By both the theoretical analysis and proposed solution for the unfairness problem, we hope to thoroughly provide an in depth view about the system design of IEEE 802.11 WLANs in the future.