速率控制WiFi网络上行流量的隐藏终端管理

Mustafa Al-Bado, C. Sengul, C. Sreenan, Kenneth N. Brown
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引用次数: 3

摘要

本文揭示了速率控制环境下上行流量隐藏终端管理中存在的几个问题,并提出了相应的解决方案。我们特别关注客户机与接入点(AP)相关联的场景。主要的挑战来自于速率控制协议和隐藏终端之间的负交互。为了揭示这些问题,我们使用一种最新的信道估计方法(CEA)将丢包的原因分为三类:噪声、拥塞和隐藏终端。我们的测试平台和基于仿真的实验表明,当mac层ACK帧以相对较高的传输速率发送时,使用CEA的隐藏终端估计的准确性会下降。为了提高CEA的准确性,结果证明了AP根据所有客户端的最小ACK率发送ACK的必要性和成本。我们提出了一种结合CEA和RTS/CTS消息的自适应方案。在轻度拥塞环境下,该方案使Minstrel速率控制算法的总吞吐量提高了60%。我们还提出了一种基于阈值的自适应RTS/CTS方案来处理高度拥塞的环境。基于阈值的自适应RTS/CTS方案将自适应RTS/CTS方案和Minstrel算法的总吞吐量提高了20-35%。最后,我们提出并评估了一种机会爆发方案,该方案在客户端之间实现了公平性。仿真结果表明,在给定的实际场景中,机会爆破算法在Jain公平性度量(0.11 ~ 0.38)上优于先前的方案和Minstrel算法。它还保持了相对较高的总体吞吐量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hidden terminal management for uplink traffic in rate-controlled WiFi networks
This paper exposes several problems in managing hidden terminals for uplink traffic in rate-controlled environments, and presents solutions to mitigate them. In particular, we focus on scenarios, in which, clients are associated with an access point (AP). The main challenge stems from the negative interactions between rate-control protocols and hidden terminals. To expose the problems, we use a recent channel estimation approach (CEA) to differentiate the reason for packet losses into three categories, noise, congestion and hidden terminals. Our testbed and simulation-based experiments show that the accuracy of hidden terminal estimations using the CEA degrades as MAC-layer ACK frames are sent with relatively high transmission rates. To improve the accuracy of the CEA, the results demonstrate the necessity and cost of making the AP send ACKs based on the minimum ACK rate of all clients. We propose an adaptive scheme that combines both the CEA and RTS/CTS messages. The proposed scheme increases the overall throughput of Minstrel rate-control algorithm by 60% in case of light congested environments. We also proposed a threshold-based adaptive RTS/CTS scheme based on the prior scheme to handle the highly congested environments. The threshold-based adaptive RTS/CTS scheme improves the overall throughput of the adaptive RTS/CTS scheme and Minstrel algorithm between 20-35%. Finally, we propose and evaluate an opportunistic burst scheme, which enforce fairness among clients. Simulation results show that opportunistic bursting outperforms the prior schemes and Minstrel algorithm in Jain's fairness metric (between 0.11 and 0.38) for a realistic given scenario. It also keeps a relatively high overall throughput.
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