SARA: Stochastic Automata Rate Adaptation for IEEE 802.11 Networks
SARA: Stochastic Automata Rate Adaptation for IEEE 802.11 Networks
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DOI：
10.1109/tpds.2007.70814
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发表时间：
2008-11
影响因子：
5.3
通讯作者：
Tarun Joshi;Disha Ahuja;Damanjit Singh;D. Agrawal
中科院分区：
文献类型：
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作者：
Tarun Joshi;Disha Ahuja;Damanjit Singh;D. Agrawal
现有的速率自适应算法可以大致分为两类：（a）基于信噪比测量的和（B）基于统计计数的。虽然前者遭受不准确的估计，后者利用预定义的阈值动态变化的速率。在本文中，我们首先分析了传输速率对无线链路性能的影响，因为性能可能会随着传输速率的增加而改善或恶化。基于此，我们提出了随机自动机速率自适应算法（SARA）。SARA受到随机学习自动机（SLA）的启发，SLA是一种用于适应随机环境的机器学习技术。SARA为每个传输速率分配选择概率。然后，它随机选择传输尝试的速率，并基于从接收机获得的反馈（ACK/NACK）动态更新概率，从而避免了对显式信道估计或预定义阈值的需要。相对于以前的工作，SARA是非常适合于固定和非固定信道环境，并与现有的IEEE 802.11 MAC标准完全兼容。我们比较了我们提出的协议的性能与自动速率回退（ARF）彻底和自适应ARF（AARF）和其他现有的协议，在不同的信道情况下。
Existing rate adaptation algorithms can broadly be classified under two categories: (a) signal to noise ratio measurement based and (b) statistical count based. While the former suffers from inaccurate estimations, the latter utilizes pre-defined thresholds for dynamically varying the rate. In this paper, we first analyze the impact of transmission rate on the performance of a wireless link as the performance may either improve or deteriorate with increasing transmission rates. Based on this observation, we then propose stochastic automata rate adaptation algorithm (SARA). SARA is inspired by stochastic learning automata (SLA), a machine learning technique for adaptation in random environments. SARA assigns a selection probability to each of the transmission rates. It then randomly selects a rate for a transmission attempt and dynamically updates the probabilities based on the obtained feedback from the receiver (ACK/NACK), thereby obviating the need for explicit channel estimation or predefined thresholds. As opposed to the previous work, SARA is ideally suited for both stationary and non-stationary channel environments and is fully compatible with the existing IEEE 802.11 MAC standard. We compare the performance of our proposed protocol with automatic rate fallback (ARF) thoroughly and adaptive ARF (AARF) and other existing protocols, under different channel scenarios.